title
12. Reinforcement Learning From Human Feedback | Andrew Ng | DeepLearning.ai - Full Course

description
This course comes from [https://learn.deeplearning.ai/reinforcement-learning-from-human-feedback/lesson/1/introduction](https://learn.deeplearning.ai/reinforcement-learning-from-human-feedback/lesson/1/introduction) Course created by Andrew Ng In this YouTube video, a technology called Reinforcement Learning From Human Feedback (RLHF) is introduced, developed in partnership with Google Cloud. The technology aims to adjust the output of large language models (LLMS) through human feedback to make them more in line with human preferences and values. The algorithm has played a key role in the rise of the LLM, especially when solving tasks that are difficult to explain or describe. Using Vertex AI, Google Cloud's machine learning platform, the course explores the concept of RLHF, the exploration of sample datasets, and the tuning and evaluation of LLMS. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '12. Reinforcement Learning From Human Feedback | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': 'Course delves into reinforcement learning from human feedback (rlhf) in machine learning, discussing its application in tuning language models, setting training parameters, and analyzing model performance to improve model quality and efficiency, with acknowledgments to contributors from google cloud and deeplearning.ai.', 'chapters': [{'end': 298.392, 'segs': [{'end': 57.718, 'src': 'embed', 'start': 25.272, 'weight': 1, 'content': [{'end': 29.775, 'text': 'This algorithm is, I think, a big deal and has been a central part to the rise of LLMs.', 'start': 25.272, 'duration': 4.503}, {'end': 37.362, 'text': "And it turns out that ROHF can be useful to you, even if you're not training an LLM from scratch,", 'start': 30.835, 'duration': 6.527}, {'end': 41.847, 'text': 'but instead building an application whose values you want to set.', 'start': 37.362, 'duration': 4.485}, {'end': 50.155, 'text': 'While fine-tuning could be one way to do this, as you learn in this course, for many cases, ROHF can be more efficient.', 'start': 42.627, 'duration': 7.528}, {'end': 57.718, 'text': 'For example, there are many valid ways in which an LLM can respond to a prompt, such as what is the capital of France?', 'start': 50.976, 'duration': 6.742}], 'summary': "Rohf algorithm is crucial for llms' rise, efficient for setting values in applications.", 'duration': 32.446, 'max_score': 25.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k25272.jpg'}, {'end': 89.2, 'src': 'embed', 'start': 68.501, 'weight': 0, 'content': [{'end': 79.404, 'text': 'and so RLHF is a method for gathering human feedback on which responses they prefer in order to train the model to generate more responses that humans prefer.', 'start': 68.501, 'duration': 10.903}, {'end': 89.2, 'text': "In this process, you start off with an LLM that's already been trained with instruction tuning, so it's already learned to follow instructions.", 'start': 80.993, 'duration': 8.207}], 'summary': 'Rlhf gathers human feedback to train the model for preferred responses.', 'duration': 20.699, 'max_score': 68.501, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k68501.jpg'}, {'end': 184.296, 'src': 'embed', 'start': 141.044, 'weight': 3, 'content': [{'end': 143.725, 'text': "I'm really excited to work with you and your team on this.", 'start': 141.044, 'duration': 2.681}, {'end': 152.796, 'text': "In this course, you'll learn about the ROHF process and also gain hands-on practice, exploring sample datasets for ROHF,", 'start': 144.232, 'duration': 8.564}, {'end': 158.699, 'text': 'tuning the LAMA2 model using ROHF and then also evaluating the newly tuned model.', 'start': 152.796, 'duration': 5.903}, {'end': 164.022, 'text': "Nikita will go through these concepts using Google Cloud's machine learning platform, Vertex AI.", 'start': 159.56, 'duration': 4.462}, {'end': 174.693, 'text': "What really excites me about RLHF is that it helps us to improve an LLM's ability to solve tasks where the desired output is difficult to explain or describe.", 'start': 164.67, 'duration': 10.023}, {'end': 178.214, 'text': "In other words, problems where there's no single correct answer.", 'start': 175.273, 'duration': 2.941}, {'end': 184.296, 'text': 'And in a lot of problems we naturally want to use LLMs for, there really is no one correct answer.', 'start': 178.234, 'duration': 6.062}], 'summary': "Learn about rohf process, lama2 model tuning, and evaluation using google cloud's vertex ai for improving llm's ability in solving tasks without a single correct answer.", 'duration': 43.252, 'max_score': 141.044, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k141044.jpg'}], 'start': 0.069, 'title': 'Reinforcement learning from human feedback in machine learning', 'summary': 'Explains reinforcement learning from human feedback (rlhf) and its significance in tuning large language models (llms) to align with human preferences, offering a more efficient alternative to fine-tuning. it also introduces rlhf in machine learning, discussing its application in tuning language models, its benefits in solving tasks with no single correct answer, and its role in improving model quality with acknowledgments to contributors from google cloud and deeplearning.ai.', 'chapters': [{'end': 114.772, 'start': 0.069, 'title': 'Reinforcement learning from human feedback', 'summary': 'Explains reinforcement learning from human feedback (rlhf) and its significance in tuning large language models (llms) to align with human preferences, offering a more efficient alternative to fine-tuning.', 'duration': 114.703, 'highlights': ["RLHF is crucial in aligning LLM's output with human preferences and values, serving as an important tuning technique.", 'ROHF can be useful for applications seeking to align with specific values, offering an efficient alternative to fine-tuning.', 'RLHF involves gathering human feedback to train the model to generate responses preferred by humans, resulting in a tuned LLM that aligns with human preferences.', "ROHF can efficiently fine-tune an instruction-tuned LLM using a dataset indicating human labelers' preferences, resulting in better-aligned outputs."]}, {'end': 298.392, 'start': 115.78, 'title': 'Rlhf in machine learning', 'summary': 'Introduces rlhf in machine learning, discussing its application in tuning language models, its benefits in solving tasks with no single correct answer, and its role in improving model quality with acknowledgments to contributors from google cloud and deeplearning.ai.', 'duration': 182.612, 'highlights': ["RLHF is a technique used to better align an LLM's output with user intention and preference, and it is applied in tuning language models for tasks like summarization.", "RLHF helps improve an LLM's ability to solve tasks where there's no single correct answer, such as in summarization tasks, allowing users to see as much as possible in a short time.", 'Acknowledgments to contributors from Google Cloud and deeplearning.ai, including Bethany Wang, Mei Hu, Jarek Kazmierczak, Eddie Xu, and Leslie Zerma, for their contributions to the course.', 'The chapter covers the practical application of RLHF in tuning the LAMA2 model using ROHF and evaluating the newly tuned model with hands-on practice and exploring sample datasets.', "RLHF is a key part of improving the quality of large language models, although it doesn't solve all problems related to truthfulness and toxicity.", 'The chapter emphasizes that no reinforcement learning knowledge is required to get started with RLHF and discusses its difference from supervised fine tuning in training machine learning models.']}], 'duration': 298.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k69.jpg', 'highlights': ["RLHF is crucial in aligning LLM's output with human preferences and values, serving as an important tuning technique.", "ROHF can efficiently fine-tune an instruction-tuned LLM using a dataset indicating human labelers' preferences, resulting in better-aligned outputs.", "RLHF is a technique used to better align an LLM's output with user intention and preference, and it is applied in tuning language models for tasks like summarization.", "RLHF helps improve an LLM's ability to solve tasks where there's no single correct answer, such as in summarization tasks, allowing users to see as much as possible in a short time.", 'The chapter covers the practical application of RLHF in tuning the LAMA2 model using ROHF and evaluating the newly tuned model with hands-on practice and exploring sample datasets.']}, {'end': 1066.1, 'segs': [{'end': 320.923, 'src': 'embed', 'start': 298.752, 'weight': 0, 'content': [{'end': 307.18, 'text': 'We can use these human-generated summaries to create pairs of input text and summary, and we could train a model directly on a bunch of these pairs.', 'start': 298.752, 'duration': 8.428}, {'end': 312.478, 'text': "But the thing is, there's no one correct way to summarize a piece of text.", 'start': 308.015, 'duration': 4.463}, {'end': 317.641, 'text': 'Natural language is flexible, and there are often many ways to say the same thing.', 'start': 313.158, 'duration': 4.483}, {'end': 320.923, 'text': "For example, here's an equally valid summary.", 'start': 317.981, 'duration': 2.942}], 'summary': 'Human-generated summaries can be used to train models for text summarization. natural language is flexible and can have many valid summaries.', 'duration': 22.171, 'max_score': 298.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k298752.jpg'}, {'end': 376.283, 'src': 'embed', 'start': 342.958, 'weight': 1, 'content': [{'end': 344.399, 'text': 'objective best answer.', 'start': 342.958, 'duration': 1.441}, {'end': 351.845, 'text': "So instead of trying to find the best summary for a particular piece of input text, we're going to frame this problem a little differently.", 'start': 344.819, 'duration': 7.026}, {'end': 362.254, 'text': "We're going to gather information on human preferences and to do that we'll provide a human labeler with two candidate summaries and ask the labeler to pick which one they prefer.", 'start': 352.145, 'duration': 10.109}, {'end': 370.298, 'text': 'And instead of the standard supervised tuning process, where we tune the model to map an input to a single correct answer,', 'start': 362.813, 'duration': 7.485}, {'end': 376.283, 'text': "we'll use reinforcement learning to tune the model to produce responses that are aligned with human preferences.", 'start': 370.298, 'duration': 5.985}], 'summary': 'Using reinforcement learning to align model responses with human preferences.', 'duration': 33.325, 'max_score': 342.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k342958.jpg'}, {'end': 415.765, 'src': 'embed', 'start': 385.149, 'weight': 3, 'content': [{'end': 387.111, 'text': 'but the high-level themes are the same.', 'start': 385.149, 'duration': 1.962}, {'end': 389.813, 'text': 'RLHF consists of three stages.', 'start': 387.691, 'duration': 2.122}, {'end': 393.174, 'text': 'First we create a preference dataset,', 'start': 390.493, 'duration': 2.681}, {'end': 403.738, 'text': 'then we use this preference dataset to train a reward model with supervised learning and then we use the reward model in a reinforcement learning loop to fine-tune our base large language model.', 'start': 393.174, 'duration': 10.564}, {'end': 411.281, 'text': "Let's look at each of these steps in detail and don't worry if you're totally new to reinforcement learning you don't need any background for this course.", 'start': 403.998, 'duration': 7.283}, {'end': 415.765, 'text': "First things first, we're going to start with the large language model that we want to tune.", 'start': 411.761, 'duration': 4.004}], 'summary': 'Rlhf consists of three stages: preference dataset creation, reward model training, and reinforcement learning for fine-tuning.', 'duration': 30.616, 'max_score': 385.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k385149.jpg'}, {'end': 500.724, 'src': 'embed', 'start': 473.535, 'weight': 7, 'content': [{'end': 478.159, 'text': "This is the dataset that we talked about earlier, and it's called a preference dataset.", 'start': 473.535, 'duration': 4.624}, {'end': 483.263, 'text': "In the next lesson you'll get a chance to take a look at one of these datasets in detail.", 'start': 478.639, 'duration': 4.624}, {'end': 492.831, 'text': "but for now the key takeaway is that the preference dataset indicates a human labeler's preference between two possible model outputs for the same input.", 'start': 483.263, 'duration': 9.568}, {'end': 500.724, 'text': "Now, it's important to note that this dataset captures the preferences of the human labelers, but not human preference in general.", 'start': 493.297, 'duration': 7.427}], 'summary': "Preference dataset captures human labelers' preferences between two model outputs for the same input.", 'duration': 27.189, 'max_score': 473.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k473535.jpg'}, {'end': 561.02, 'src': 'embed', 'start': 537.472, 'weight': 4, 'content': [{'end': 546.416, 'text': 'we want this reward model to take in a prompt and a completion and return a scalar value that indicates how good that completion is for the given prompt.', 'start': 537.472, 'duration': 8.944}, {'end': 549.697, 'text': 'So the reward model is essentially a regression model.', 'start': 546.956, 'duration': 2.741}, {'end': 550.798, 'text': 'It outputs numbers.', 'start': 549.937, 'duration': 0.861}, {'end': 555.456, 'text': 'The reward model is trained on the preference dataset,', 'start': 552.654, 'duration': 2.802}, {'end': 561.02, 'text': 'using the triplets of prompt and two completions the winning candidate and the losing candidate.', 'start': 555.456, 'duration': 5.564}], 'summary': 'Reward model assesses completion quality for prompts using preference dataset.', 'duration': 23.548, 'max_score': 537.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k537472.jpg'}, {'end': 629.935, 'src': 'embed', 'start': 603.234, 'weight': 5, 'content': [{'end': 610.516, 'text': 'Our goal here is to tune the base large language model to produce completions that will maximize the reward given by the reward model.', 'start': 603.234, 'duration': 7.282}, {'end': 617.378, 'text': 'So if the base LLM produces completions that better align with the preferences of the people who labeled the data,', 'start': 610.876, 'duration': 6.502}, {'end': 620.379, 'text': 'then it will receive higher rewards from the reward model.', 'start': 617.378, 'duration': 3.001}, {'end': 624.786, 'text': 'To do this, we introduce a second data set, our prompt data set.', 'start': 621.019, 'duration': 3.767}, {'end': 629.935, 'text': 'This is just, as the name implies, a data set of prompts, no completions.', 'start': 625.467, 'duration': 4.468}], 'summary': 'Tune large language model to align with labeled data, maximizing rewards.', 'duration': 26.701, 'max_score': 603.234, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k603234.jpg'}, {'end': 916.445, 'src': 'embed', 'start': 889.264, 'weight': 6, 'content': [{'end': 894.848, 'text': 'But because large language models are so large, updating all of the many weights can take a very long time.', 'start': 889.264, 'duration': 5.584}, {'end': 898.25, 'text': 'Instead, we can try out parameter efficient fine tuning,', 'start': 895.188, 'duration': 3.062}, {'end': 906.136, 'text': 'which is a research area that aims to reduce the challenges of fine tuning large language models by only training a small subset of model parameters.', 'start': 898.25, 'duration': 7.886}, {'end': 912.403, 'text': 'These parameters might be a subset of the existing model parameters, or they could be an entirely new set of parameters.', 'start': 906.601, 'duration': 5.802}, {'end': 916.445, 'text': 'Figuring out the optimal methodology is an active area of research,', 'start': 912.723, 'duration': 3.722}], 'summary': 'Efficient fine-tuning reduces training time for large language models.', 'duration': 27.181, 'max_score': 889.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k889264.jpg'}], 'start': 298.752, 'title': 'Summarization challenges and rlhf', 'summary': 'Discusses challenges in summarizing text due to natural language flexibility, and proposes a new approach to gather information on human preferences. it also outlines the process of rlhf, emphasizing its implementation with parameter efficient tuning.', 'chapters': [{'end': 362.254, 'start': 298.752, 'title': 'Summarization techniques', 'summary': 'Discusses the challenges in summarizing text due to the flexibility of natural language, the existence of multiple valid summaries, and the subjective nature of human preferences, proposing a new approach to gather information on human preferences for training models.', 'duration': 63.502, 'highlights': ['The flexibility of natural language and the existence of multiple valid summaries make it challenging to find the best summary for a piece of text.', 'The subjective nature of human preferences poses a challenge in quantifying the best summary for a given piece of input text.', 'Gathering information on human preferences by providing a human labeler with two candidate summaries helps in understanding the diverse preferences of different people or groups.']}, {'end': 1066.1, 'start': 362.813, 'title': 'Reinforcement learning from human feedback', 'summary': 'Outlines the process of reinforcement learning from human feedback (rlhf) including creating a preference dataset, training a reward model, and using reinforcement learning to fine-tune a base large language model, with the key takeaway being its implementation with parameter efficient tuning.', 'duration': 703.287, 'highlights': ['RLHF consists of three stages: creating a preference dataset, training a reward model, and using reinforcement learning to fine-tune the base large language model. The process of RLHF involves three main steps: creating a preference dataset, training a reward model, and using reinforcement learning to fine-tune the base large language model.', 'The reward model, trained on the preference dataset, produces a score indicating how well a completion aligns with human preferences. The reward model, trained on the preference dataset, outputs a score indicating the alignment of model completions with human preferences.', 'Reinforcement learning is used to guide the base large language model in producing completions that maximize the reward given by the reward model. Reinforcement learning is employed to guide the base large language model in generating completions that maximize the reward given by the reward model.', "The implementation of RLHF utilizes parameter efficient fine-tuning to update only a smaller subset of the base large language model's weights. RLHF is implemented using parameter efficient fine-tuning, updating only a smaller subset of the base large language model's weights.", 'The process involves creating and using two datasets: a preference dataset and a prompt dataset. The process involves the use of two datasets: a preference dataset and a prompt dataset, for the training and fine-tuning of the large language model.']}], 'duration': 767.348, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k298752.jpg', 'highlights': ['The flexibility of natural language and the existence of multiple valid summaries make it challenging to find the best summary for a piece of text.', 'The subjective nature of human preferences poses a challenge in quantifying the best summary for a given piece of input text.', 'Gathering information on human preferences by providing a human labeler with two candidate summaries helps in understanding the diverse preferences of different people or groups.', 'RLHF consists of three stages: creating a preference dataset, training a reward model, and using reinforcement learning to fine-tune the base large language model.', 'The reward model, trained on the preference dataset, produces a score indicating how well a completion aligns with human preferences.', 'Reinforcement learning is used to guide the base large language model in producing completions that maximize the reward given by the reward model.', "The implementation of RLHF utilizes parameter efficient fine-tuning to update only a smaller subset of the base large language model's weights.", 'The process involves creating and using two datasets: a preference dataset and a prompt dataset.']}, {'end': 1460.385, 'segs': [{'end': 1176.152, 'src': 'embed', 'start': 1145.034, 'weight': 0, 'content': [{'end': 1147.816, 'text': "So we're going to take a look at another sample in this dataset.", 'start': 1145.034, 'duration': 2.782}, {'end': 1150.918, 'text': 'And this one also ends with summary colon.', 'start': 1148.236, 'duration': 2.682}, {'end': 1155.04, 'text': 'So all of our examples in this dataset, all of the prompts end this way.', 'start': 1151.358, 'duration': 3.682}, {'end': 1161.344, 'text': 'And the reason this is important is because you need your dataset examples to match your expected production traffic.', 'start': 1155.28, 'duration': 6.064}, {'end': 1162.965, 'text': 'So during training.', 'start': 1161.684, 'duration': 1.281}, {'end': 1169.829, 'text': 'this data set here contains the specific formatting or specific keyword or instruction of summary,', 'start': 1162.965, 'duration': 6.864}, {'end': 1176.152, 'text': "and it's important that at inference time our data set should be formatted in the same way and contain the same instructions.", 'start': 1169.829, 'duration': 6.323}], 'summary': 'Dataset examples must match production traffic for successful inference.', 'duration': 31.118, 'max_score': 1145.034, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1145034.jpg'}, {'end': 1284.235, 'src': 'embed', 'start': 1261.268, 'weight': 4, 'content': [{'end': 1268.313, 'text': 'so in this case we would refer to candidate one as being the winning candidate and we would call candidate zero the losing candidate,', 'start': 1261.268, 'duration': 7.045}, {'end': 1270.835, 'text': 'since candidate one was preferred by the human labeler.', 'start': 1268.313, 'duration': 2.522}, {'end': 1279.09, 'text': 'So this is what the labeler of this particular example thought was the better summary, but you might have a different preference.', 'start': 1271.303, 'duration': 7.787}, {'end': 1284.235, 'text': 'So take a minute and read through this entire input text here and see if you agree.', 'start': 1279.33, 'duration': 4.905}], 'summary': 'Candidate one is the winning candidate preferred by the human labeler.', 'duration': 22.967, 'max_score': 1261.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1261268.jpg'}, {'end': 1320.657, 'src': 'embed', 'start': 1297.422, 'weight': 3, 'content': [{'end': 1305.526, 'text': 'Picking the right labelers and making sure you provide the right criteria for your specific problem is difficult and it depends a lot on your use case.', 'start': 1297.422, 'duration': 8.104}, {'end': 1308.187, 'text': 'But this is essentially what the preference dataset looks like.', 'start': 1305.806, 'duration': 2.381}, {'end': 1314.174, 'text': "We're going to train our reward model on these triplets of our input text, which again is the prompt,", 'start': 1308.612, 'duration': 5.562}, {'end': 1316.695, 'text': 'and then the winning candidate and the losing candidate.', 'start': 1314.174, 'duration': 2.521}, {'end': 1320.657, 'text': "And when we do that, we'll get a scalar value indicating how good the completion is.", 'start': 1316.915, 'duration': 3.742}], 'summary': 'Selecting labelers and criteria is crucial for training a reward model on preference dataset to evaluate text completion.', 'duration': 23.235, 'max_score': 1297.422, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1297422.jpg'}, {'end': 1405.053, 'src': 'embed', 'start': 1381.561, 'weight': 5, 'content': [{'end': 1389.845, 'text': "So we're just loading in six examples of our much larger prompt data set that we'll use in the next lesson when we actually tune the base large language model.", 'start': 1381.561, 'duration': 8.284}, {'end': 1393.027, 'text': 'Now, a quick note on your prompts in this data set.', 'start': 1390.105, 'duration': 2.922}, {'end': 1398.97, 'text': 'It is important that the prompts in the preference data set and this prompt data set come from the same distribution.', 'start': 1393.487, 'duration': 5.483}, {'end': 1405.053, 'text': 'In this case, all the prompts are a data set of Reddit posts, so they do come from the same distribution.', 'start': 1399.23, 'duration': 5.823}], 'summary': 'Loading six examples from reddit posts for language model tuning.', 'duration': 23.492, 'max_score': 1381.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1381561.jpg'}], 'start': 1066.38, 'title': 'Data set formatting and training reward model', 'summary': "Discusses the structure of a preference data list, emphasizing the significance of 'summary' keyword, and the use of preference and prompt datasets in training the reward model and tuning the language model.", 'chapters': [{'end': 1183.376, 'start': 1066.38, 'title': 'Data set formatting and instructions', 'summary': "Discusses the structure of a preference data list, highlighting the presence of a specific keyword 'summary' in the dataset examples and its importance for matching the expected production traffic during training.", 'duration': 116.996, 'highlights': ["The preference data list contains examples with prompts ending with 'summary colon', indicating the specific formatting required for the dataset and its importance for matching expected production traffic during training.", "The sample dataset includes prompts such as 'I live right next to a huge university. I've been applying for a variety of jobs' and ends with 'summary colon', reinforcing the consistent structure of the dataset.", "The dataset's consistent use of the 'summary' indicator in prompts ensures that the dataset examples match the expected production traffic during training and inference."]}, {'end': 1460.385, 'start': 1183.636, 'title': 'Training reward model and prompt dataset', 'summary': "Discusses the preference dataset used to train the reward model, demonstrating a human labeler's choice between two candidate summaries and highlights the importance of the prompt dataset in tuning the base large language model.", 'duration': 276.749, 'highlights': ['The preference dataset is used to train the reward model on triplets of input text, winning candidate, and losing candidate. The preference dataset trains the reward model on triplets of input text, winning candidate, and losing candidate.', "The human labeler's choice of the winning candidate is indicated by the value in the choice key, with candidate one being preferred in the given example. The human labeler's choice of the winning candidate is indicated by the value in the choice key, with candidate one being preferred in the given example.", 'The prompt dataset, from the same distribution as the preference dataset, is crucial for tuning the base large language model. The prompt dataset, from the same distribution as the preference dataset, is crucial for tuning the base large language model.']}], 'duration': 394.005, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1066380.jpg', 'highlights': ["The preference data list contains examples with prompts ending with 'summary colon', indicating the specific formatting required for the dataset and its importance for matching expected production traffic during training.", "The dataset's consistent use of the 'summary' indicator in prompts ensures that the dataset examples match the expected production traffic during training and inference.", "The sample dataset includes prompts such as 'I live right next to a huge university. I've been applying for a variety of jobs' and ends with 'summary colon', reinforcing the consistent structure of the dataset.", 'The preference dataset is used to train the reward model on triplets of input text, winning candidate, and losing candidate.', "The human labeler's choice of the winning candidate is indicated by the value in the choice key, with candidate one being preferred in the given example.", 'The prompt dataset, from the same distribution as the preference dataset, is crucial for tuning the base large language model.']}, {'end': 2130.103, 'segs': [{'end': 1488.602, 'src': 'embed', 'start': 1460.606, 'weight': 5, 'content': [{'end': 1468.69, 'text': 'So this looks fairly similar to the preference dataset, but we just have one single key, which is the input text field, AKA the prompt.', 'start': 1460.606, 'duration': 8.084}, {'end': 1474.913, 'text': 'So if we take a look at another example in this dataset, we can use the same printD function.', 'start': 1468.85, 'duration': 6.063}, {'end': 1480.899, 'text': "And this time we'll just extract the second element in this list.", 'start': 1477.577, 'duration': 3.322}, {'end': 1488.602, 'text': "And if we print this again, you can see that there's only one key and that key is called input text and the corresponding value is a prompt.", 'start': 1481.079, 'duration': 7.523}], 'summary': "Dataset has single key 'input text' and corresponding values, used for extraction.", 'duration': 27.996, 'max_score': 1460.606, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1460606.jpg'}, {'end': 1555.238, 'src': 'embed', 'start': 1525.393, 'weight': 3, 'content': [{'end': 1530.537, 'text': "Now that we've covered some of the basic concepts of RLHF and we've taken a look at the data,", 'start': 1525.393, 'duration': 5.144}, {'end': 1535.38, 'text': "we're finally ready to kick off that RLHF workflow and tune a large language model.", 'start': 1530.537, 'duration': 4.843}, {'end': 1541.425, 'text': "To do all of this, we're going to be using Vertex AI, which is Google Cloud's machine learning platform.", 'start': 1535.981, 'duration': 5.444}, {'end': 1542.626, 'text': "Let's get started.", 'start': 1541.905, 'duration': 0.721}, {'end': 1548.471, 'text': 'RLHF tuning jobs on Vertex AI run as Vertex AI pipelines.', 'start': 1543.786, 'duration': 4.685}, {'end': 1555.238, 'text': 'In machine learning, pipelines are portable and scalable machine learning workflows that are based on containers.', 'start': 1549.192, 'duration': 6.046}], 'summary': 'Tuning a large language model using vertex ai for rlhf workflow.', 'duration': 29.845, 'max_score': 1525.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1525393.jpg'}, {'end': 1596.09, 'src': 'embed', 'start': 1572.333, 'weight': 1, 'content': [{'end': 1582.578, 'text': 'and a pipeline turns out to be a convenient way of encapsulating all of these many steps into one single object to help you automate and reproduce your machine learning workflow.', 'start': 1572.333, 'duration': 10.245}, {'end': 1589.863, 'text': "Now I'm not gonna spend too much time talking about pipelines here, since you don't need to write your own pipeline.", 'start': 1583.536, 'duration': 6.327}, {'end': 1591.725, 'text': "you're just using an existing pipeline.", 'start': 1589.863, 'duration': 1.862}, {'end': 1596.09, 'text': 'But to make things a little more concrete, here is a basic machine learning pipeline.', 'start': 1591.926, 'duration': 4.164}], 'summary': 'A pipeline streamlines multiple steps into one object for easy automation and reproducibility in machine learning.', 'duration': 23.757, 'max_score': 1572.333, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1572333.jpg'}, {'end': 1676.292, 'src': 'embed', 'start': 1635.735, 'weight': 0, 'content': [{'end': 1639.677, 'text': 'A reinforcement learning from human feedback pipeline is a little more complicated.', 'start': 1635.735, 'duration': 3.942}, {'end': 1641.218, 'text': 'It might look something like this.', 'start': 1640.077, 'duration': 1.141}, {'end': 1643.821, 'text': 'We first create a preference data set.', 'start': 1641.499, 'duration': 2.322}, {'end': 1646.844, 'text': 'That preference data set is used to train a reward model.', 'start': 1644.021, 'duration': 2.823}, {'end': 1653.291, 'text': 'The reward model is used with the prompt data set to tune the base large language model with reinforcement learning.', 'start': 1647.325, 'duration': 5.966}, {'end': 1658.577, 'text': 'And then we get a tuned large language model and some output and training curves as well.', 'start': 1653.912, 'duration': 4.665}, {'end': 1664.942, 'text': "In reality, the pipeline that we're going to execute has a lot more steps, but more on that shortly.", 'start': 1658.917, 'duration': 6.025}, {'end': 1671.128, 'text': 'The RLHF pipeline exists in the OSS Google Cloud Pipelines components library.', 'start': 1665.243, 'duration': 5.885}, {'end': 1676.292, 'text': "So to run this pipeline, you'll first import it, then you'll compile it, and then execute it.", 'start': 1671.348, 'duration': 4.944}], 'summary': 'Reinforcement learning from human feedback pipeline uses preference data set to train reward model and tune large language model with rl, existing in oss google cloud pipelines.', 'duration': 40.557, 'max_score': 1635.735, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1635735.jpg'}, {'end': 1805.116, 'src': 'embed', 'start': 1771.596, 'weight': 7, 'content': [{'end': 1774.077, 'text': 'So this uses the compiler.', 'start': 1771.596, 'duration': 2.481}, {'end': 1777.178, 'text': 'This is what we imported from Kubeflow Pipelines up here.', 'start': 1774.097, 'duration': 3.081}, {'end': 1783.241, 'text': 'And then we call compiler and we call the compile function.', 'start': 1777.198, 'duration': 6.043}, {'end': 1784.882, 'text': 'So a whole lot of compiling here.', 'start': 1783.421, 'duration': 1.461}, {'end': 1789.665, 'text': "But what we're really doing here is we're passing in two elements to this compile function.", 'start': 1785.002, 'duration': 4.663}, {'end': 1791.806, 'text': 'The first is RLHF pipeline.', 'start': 1789.845, 'duration': 1.961}, {'end': 1798.11, 'text': 'And that is the pipeline that we imported earlier from the Google Cloud Pipeline Components library.', 'start': 1792.586, 'duration': 5.524}, {'end': 1805.116, 'text': 'The next thing we pass in is the package path right here, which is the path to our YAML file.', 'start': 1798.13, 'duration': 6.986}], 'summary': 'Using the compiler to compile two elements for rlhf pipeline.', 'duration': 33.52, 'max_score': 1771.596, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1771596.jpg'}, {'end': 2058.505, 'src': 'embed', 'start': 2032.239, 'weight': 4, 'content': [{'end': 2040.369, 'text': 'So in the previous lesson, when we took a look at these different data sets, we were just loading in small JSONL files directly into memory.', 'start': 2032.239, 'duration': 8.13}, {'end': 2047.838, 'text': 'But for this actual pipeline, our data sets are much larger and they need to exist somewhere called Google Cloud Storage.', 'start': 2040.569, 'duration': 7.269}, {'end': 2050.581, 'text': "Cloud Storage is Google Cloud's object storage.", 'start': 2048.139, 'duration': 2.442}, {'end': 2058.505, 'text': 'That means that you can store images, CSV files, text files, save model artifacts, JSONL files, just about anything.', 'start': 2050.942, 'duration': 7.563}], 'summary': 'Data sets are stored in google cloud storage for larger pipelines.', 'duration': 26.266, 'max_score': 2032.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2032239.jpg'}], 'start': 1460.606, 'title': 'Data set of prompts and rlhf workflow', 'summary': "Discusses a data set of prompts and their corresponding values, illustrating the dataset's structure. it also introduces the rlhf tuning workflow using vertex ai on google cloud and explains the use of pipelines and data sets in the process.", 'chapters': [{'end': 1501.71, 'start': 1460.606, 'title': 'Data set of prompts', 'summary': "Discusses a data set of prompts with a single key 'input text field' and 'input text' as the key with corresponding prompt values, illustrating the structure and content of the dataset.", 'duration': 41.104, 'highlights': ['The dataset consists of prompts with a single key, the input text field, and corresponding values as prompts.', 'The example demonstrates the extraction of the second element in the dataset using the printD function.', "The prompt dataset contains entries such as 'I love my health class. My teacher was amazing. Most days we just went outside, et cetera, et cetera.'"]}, {'end': 2130.103, 'start': 1501.99, 'title': 'Rlhf workflow and pipeline on vertex ai', 'summary': 'Introduces the rlhf tuning workflow using vertex ai on google cloud, explaining the use of pipelines and data sets in the process.', 'duration': 628.113, 'highlights': ['RLHF tuning jobs on Vertex AI run as Vertex AI pipelines. The Vertex AI platform is used to run RLHF tuning jobs, which are executed as Vertex AI pipelines.', 'The pipeline encapsulates multiple steps into a single object to automate and reproduce the machine learning workflow. Pipelines in machine learning encapsulate multiple steps into a single object, aiding in automation and reproducibility of the workflow.', 'The RLHF pipeline consists of multiple steps, including creating preference and prompt datasets, training reward and base language models, and executing reinforcement learning loops. The RLHF pipeline involves creating preference and prompt datasets, training reward and base language models, and executing reinforcement learning loops.', 'The RLHF pipeline exists in the OSS Google Cloud Pipelines components library. The RLHF pipeline is available in the OSS Google Cloud Pipelines components library, and to use it, one needs to import, compile, and execute it.', 'The pipeline is written using the Kubeflow Pipelines OSS library and compiled to create a YAML file, which includes all the information needed to execute the pipeline. The pipeline is written using the Kubeflow Pipelines OSS library, and the compile function is used to create a YAML file containing all the information for executing the pipeline.', 'The RLHF pipeline visualization tool provided by Vertex AI allows users to view all components and steps of the pipeline. Vertex AI provides a visualization tool for the RLHF pipeline, enabling users to view all components and steps of the pipeline.', 'Cloud Storage is used to store the datasets in JSON lines format, and specific paths to the datasets are passed as parameters to the pipeline. The datasets are stored in Cloud Storage in JSON lines format, and their paths are passed as parameters to the pipeline.']}], 'duration': 669.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k1460606.jpg', 'highlights': ['The RLHF pipeline involves creating preference and prompt datasets, training reward and base language models, and executing reinforcement learning loops.', 'The pipeline encapsulates multiple steps into a single object to automate and reproduce the machine learning workflow.', 'The RLHF pipeline is available in the OSS Google Cloud Pipelines components library, and to use it, one needs to import, compile, and execute it.', 'Vertex AI provides a visualization tool for the RLHF pipeline, enabling users to view all components and steps of the pipeline.', 'The datasets are stored in Cloud Storage in JSON lines format, and their paths are passed as parameters to the pipeline.', 'The example demonstrates the extraction of the second element in the dataset using the printD function.', 'The dataset consists of prompts with a single key, the input text field, and corresponding values as prompts.', 'The RLHF pipeline is written using the Kubeflow Pipelines OSS library, and the compile function is used to create a YAML file containing all the information for executing the pipeline.', 'RLHF tuning jobs on Vertex AI run as Vertex AI pipelines. The Vertex AI platform is used to run RLHF tuning jobs, which are executed as Vertex AI pipelines.']}, {'end': 2597.942, 'segs': [{'end': 2154.381, 'src': 'embed', 'start': 2130.103, 'weight': 0, 'content': [{'end': 2136.145, 'text': 'as well as how to figure out what the specific path is that starts with GS colon slash slash.', 'start': 2130.103, 'duration': 6.042}, {'end': 2140.188, 'text': 'For now, we can just use these data sets that I have uploaded for you already.', 'start': 2136.465, 'duration': 3.723}, {'end': 2145.052, 'text': "The next parameter we're going to set is called large model reference.", 'start': 2140.649, 'duration': 4.403}, {'end': 2148.776, 'text': 'So in this case, we are going to set this to LAMA27B.', 'start': 2145.313, 'duration': 3.463}, {'end': 2154.381, 'text': 'Large model reference specifies which large language model we want to tune.', 'start': 2150.017, 'duration': 4.364}], 'summary': 'Setting large model reference to lama27b for language model tuning.', 'duration': 24.278, 'max_score': 2130.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2130103.jpg'}, {'end': 2210.29, 'src': 'embed', 'start': 2181.073, 'weight': 1, 'content': [{'end': 2184.455, 'text': 'The value to set here depends on the size of your preference data set.', 'start': 2181.073, 'duration': 3.382}, {'end': 2193.28, 'text': 'From experimentation, we found that the Model ideally should train over the preference data set from around 20 to 30 epochs for best results.', 'start': 2184.715, 'duration': 8.565}, {'end': 2201.365, 'text': 'And then reinforcement learning train steps is the parameter that sets the number of reinforcement learning steps to perform when tuning the base model.', 'start': 2193.941, 'duration': 7.424}, {'end': 2205.748, 'text': 'This depends on the size of your prompt data set and from experimentation.', 'start': 2201.825, 'duration': 3.923}, {'end': 2210.29, 'text': 'In this case, we found that the model should train over the prompt data set for around 10 to 20 epochs.', 'start': 2205.828, 'duration': 4.462}], 'summary': 'For best results, train model over preference data set for 20-30 epochs, and over prompt data set for 10-20 epochs.', 'duration': 29.217, 'max_score': 2181.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2181073.jpg'}, {'end': 2247.07, 'src': 'embed', 'start': 2222.859, 'weight': 3, 'content': [{'end': 2228.886, 'text': 'So if you need a handy heuristic to help you go from epochs to steps, I can show you that in the notebook.', 'start': 2222.859, 'duration': 6.027}, {'end': 2236.738, 'text': "The first thing you'll do is set the size of your dataset, and this could be for the preference dataset or the prompt dataset.", 'start': 2229.326, 'duration': 7.412}, {'end': 2244.588, 'text': "So let's say to make this a little bit easier to understand, let's say that our dataset size is 128.", 'start': 2237.299, 'duration': 7.289}, {'end': 2247.07, 'text': "Then we'll need to set the size of our batches.", 'start': 2244.588, 'duration': 2.482}], 'summary': 'To convert epochs to steps, set dataset size and batch size, e.g. 128, in the notebook.', 'duration': 24.211, 'max_score': 2222.859, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2222859.jpg'}, {'end': 2414.163, 'src': 'embed', 'start': 2387.135, 'weight': 6, 'content': [{'end': 2393.676, 'text': 'These pipelines run for many hours, so running them first on a small amount of data is just a useful thing to do.', 'start': 2387.135, 'duration': 6.541}, {'end': 2402.498, 'text': 'So in this case, my preference data set was size 3000 and the batch size is, of course, fixed at 64.', 'start': 2394.036, 'duration': 8.462}, {'end': 2408.881, 'text': 'so that helped me get my steps per epoch and then i decided to train over 30 epochs.', 'start': 2402.498, 'duration': 6.383}, {'end': 2414.163, 'text': 'so once i had that, i knew that my number of training steps for the reward model was 1410..', 'start': 2408.881, 'duration': 5.282}], 'summary': 'Pipelines run for many hours, trained on a 3000-size dataset with batch size 64 over 30 epochs, resulting in 1410 training steps for the reward model.', 'duration': 27.028, 'max_score': 2387.135, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2387135.jpg'}, {'end': 2498.416, 'src': 'embed', 'start': 2465.453, 'weight': 5, 'content': [{'end': 2467.734, 'text': 'And I have the defaults set here already.', 'start': 2465.453, 'duration': 2.281}, {'end': 2472.255, 'text': "That's one for both of the multipliers and 0.1 for the KL coefficient.", 'start': 2467.914, 'duration': 4.341}, {'end': 2487.751, 'text': 'The reward model learning rate multiplier and reinforcement learning rate multipliers are constants that you can use to adjust the base learning rate when either training the reward model or during the reinforcement learning loop.', 'start': 2474.864, 'duration': 12.887}, {'end': 2492.694, 'text': "You can't actually adjust the learning rate itself, and that's because, generally,", 'start': 2487.871, 'duration': 4.823}, {'end': 2498.416, 'text': 'you want the learning rate to match the learning rate that was used to train the base large language model.', 'start': 2492.694, 'duration': 5.722}], 'summary': 'Adjust default multipliers for reward model and reinforcement learning rates.', 'duration': 32.963, 'max_score': 2465.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2465453.jpg'}, {'end': 2597.942, 'src': 'embed', 'start': 2574.924, 'weight': 4, 'content': [{'end': 2582.888, 'text': 'and the KL coefficient essentially helps to prevent reward hacking by preventing the model from diverging too far from the original model.', 'start': 2574.924, 'duration': 7.964}, {'end': 2594.238, 'text': 'So the tuned model essentially is penalized if it starts to diverge too far from its initial distribution and break the functionality of the original large language model.', 'start': 2583.128, 'duration': 11.11}, {'end': 2597.942, 'text': 'If you set this KL coefficient to zero, there is no penalty at all.', 'start': 2594.598, 'duration': 3.344}], 'summary': 'Kl coefficient prevents reward hacking, penalizes model for divergence, set to zero for no penalty.', 'duration': 23.018, 'max_score': 2574.924, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2574924.jpg'}], 'start': 2130.103, 'title': 'Setting training and tuning pipeline parameters', 'summary': 'Covers setting training parameters for rlhf process, including large model reference, training steps, batch size, and dataset size, with specific recommendations. it also explains the impact of learning rate multipliers, kl coefficients, and their influence on preventing reward hacking, allowing adjustments for tuning the pipeline.', 'chapters': [{'end': 2443.986, 'start': 2130.103, 'title': 'Setting training parameters for rlhf process', 'summary': 'Covers setting training parameters for rlhf process including large model reference, training steps, batch size, and dataset size, with specific recommendations for the number of steps and epochs based on dataset size and model type.', 'duration': 313.883, 'highlights': ['The value of large model reference specifies which large language model to tune, with options including LAMA27B, TextBison, and the T5X family of models.', 'The number of steps to train the reward model ideally ranges from 20 to 30 epochs for best results, based on the size of the preference data set.', 'The reinforcement learning train steps parameter sets the number of reinforcement learning steps to perform when tuning the base model, with a recommendation to train over the prompt data set for around 10 to 20 epochs.', 'A handy heuristic is provided to determine the total number of training steps by setting the size of the data set, fixed batch size, and the number of epochs to train over.', 'Executing the pipeline on a smaller subset of the data, such as a preference data set of size 3000 and a prompt data set of size 2000, with fixed batch size at 64, is recommended as a best practice before training on the entire data set.']}, {'end': 2597.942, 'start': 2444.488, 'title': 'Tuning pipeline parameters for language model', 'summary': 'Explains the learning rate multipliers, kl coefficients, and their impact on preventing reward hacking, recommending default settings but allowing adjustments for tuning the pipeline.', 'duration': 153.454, 'highlights': ['The KL coefficient prevents reward hacking by penalizing the model if it diverges from its initial distribution, with no penalty when set to zero.', 'The learning rate multipliers adjust the base learning rate during reward model training or reinforcement learning loop, with potential impact on gradient updates magnitude.', 'The defaults for the learning rate multipliers and KL coefficient are 1 for both multipliers and 0.1 for the KL coefficient, suitable for initial use but adjustable for specific use cases.']}], 'duration': 467.839, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2130103.jpg', 'highlights': ['The value of large model reference specifies which large language model to tune, with options including LAMA27B, TextBison, and the T5X family of models.', 'The number of steps to train the reward model ideally ranges from 20 to 30 epochs for best results, based on the size of the preference data set.', 'The reinforcement learning train steps parameter sets the number of reinforcement learning steps to perform when tuning the base model, with a recommendation to train over the prompt data set for around 10 to 20 epochs.', 'A handy heuristic is provided to determine the total number of training steps by setting the size of the data set, fixed batch size, and the number of epochs to train over.', 'The KL coefficient prevents reward hacking by penalizing the model if it diverges from its initial distribution, with no penalty when set to zero.', 'The learning rate multipliers adjust the base learning rate during reward model training or reinforcement learning loop, with potential impact on gradient updates magnitude.', 'Executing the pipeline on a smaller subset of the data, such as a preference data set of size 3000 and a prompt data set of size 2000, with fixed batch size at 64, is recommended as a best practice before training on the entire data set.']}, {'end': 3110.286, 'segs': [{'end': 2668.4, 'src': 'embed', 'start': 2640.639, 'weight': 2, 'content': [{'end': 2643.501, 'text': 'to summarize the text in less than 50 words.', 'start': 2640.639, 'duration': 2.862}, {'end': 2650.187, 'text': "If we did include this instruction already in our dataset, we wouldn't need to set this instruction parameter.", 'start': 2643.781, 'duration': 6.406}, {'end': 2655.531, 'text': 'Because these base models have been trained over a large variety of different instructions,', 'start': 2650.427, 'duration': 5.104}, {'end': 2661.775, 'text': 'you can make this instruction parameter a simple and intuitive description of the task that you want the model to complete.', 'start': 2655.531, 'duration': 6.244}, {'end': 2668.4, 'text': 'But with that, we have wrapped up all of the parameter values that we need, and we are ready to actually execute this pipeline.', 'start': 2661.915, 'duration': 6.485}], 'summary': 'Instruction parameter can be simplified for executing the pipeline.', 'duration': 27.761, 'max_score': 2640.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2640639.jpg'}, {'end': 2721.161, 'src': 'embed', 'start': 2683.49, 'weight': 3, 'content': [{'end': 2687.253, 'text': 'For example, write a response to the following text', 'start': 2683.49, 'duration': 3.763}, {'end': 2691.196, 'text': 'And in that case, your texts that you might have could be the Reddit post.', 'start': 2687.753, 'duration': 3.443}, {'end': 2696.812, 'text': 'Now that we have all of our parameter values defined, we are ready to create a pipeline job.', 'start': 2691.69, 'duration': 5.122}, {'end': 2703.194, 'text': 'What this means is that this reinforcement learning from human feedback pipeline is going to execute on Vertex AI.', 'start': 2697.092, 'duration': 6.102}, {'end': 2708.696, 'text': "So it's not gonna run locally here in our notebook, but it's gonna run on some server on Google Cloud.", 'start': 2703.314, 'duration': 5.382}, {'end': 2714.979, 'text': 'In order to do this, we first need to authenticate to Google Cloud and initialize the Vertex AI Python SDK.', 'start': 2709.017, 'duration': 5.962}, {'end': 2717.52, 'text': "For this course, we've done that setup for you.", 'start': 2715.339, 'duration': 2.181}, {'end': 2721.161, 'text': 'But if you want to learn how to do this for yourself and your own projects,', 'start': 2717.96, 'duration': 3.201}], 'summary': 'Creating a pipeline job on vertex ai to execute reinforcement learning from human feedback on google cloud.', 'duration': 37.671, 'max_score': 2683.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2683490.jpg'}, {'end': 2856.807, 'src': 'embed', 'start': 2824.825, 'weight': 0, 'content': [{'end': 2835.231, 'text': "And so I'm going to call this job and we'll call AI platform dot pipeline job.", 'start': 2824.825, 'duration': 10.406}, {'end': 2840.994, 'text': "And to this pipeline job, I'm going to pass in a few key parameters.", 'start': 2835.251, 'duration': 5.743}, {'end': 2851.742, 'text': "So the, The first thing we'll pass in is a display name, and this is just any string name for what you want to call this pipeline job.", 'start': 2841.374, 'duration': 10.368}, {'end': 2856.807, 'text': "So here I'm calling it tutorial RLHF tuning, but you could change this to be anything you like.", 'start': 2851.862, 'duration': 4.945}], 'summary': 'Setting up an ai platform pipeline job with customizable parameters.', 'duration': 31.982, 'max_score': 2824.825, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2824825.jpg'}, {'end': 3012.135, 'src': 'embed', 'start': 2988.155, 'weight': 1, 'content': [{'end': 2996.118, 'text': 'Our ultimate goal is to create a new large language model that performs the task we care about better than the original large language model.', 'start': 2988.155, 'duration': 7.963}, {'end': 2999.139, 'text': 'So in this final lesson of this course,', 'start': 2996.758, 'duration': 2.381}, {'end': 3005.041, 'text': "we're going to discuss some different strategies for evaluation and take a look at results from the newly tuned model.", 'start': 2999.139, 'duration': 5.902}, {'end': 3006.201, 'text': "Let's get started.", 'start': 3005.541, 'duration': 0.66}, {'end': 3012.135, 'text': 'There are a few different things we can look at when evaluating large language models,', 'start': 3007.91, 'duration': 4.225}], 'summary': 'Goal: create a new large language model to outperform the original model. final lesson covers evaluation strategies and results.', 'duration': 23.98, 'max_score': 2988.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2988155.jpg'}], 'start': 2598.182, 'title': 'Parameter values in text summarization and ai pipeline deployment in europe west 4', 'summary': 'Discusses parameter values for text summarization, including the instruction parameter and execution on vertex ai. it also details deploying a reinforcement learning from human feedback pipeline in europe west 4, covering creating and running the pipeline job, evaluating large language models, and mentioning the time taken for a job on the full giant reddit data set.', 'chapters': [{'end': 2741.43, 'start': 2598.182, 'title': 'Parameter values in text summarization', 'summary': 'Discusses the parameter values for text summarization, including the instruction parameter, which needs to be set to instruct the model on the task it needs to perform. it also highlights the execution of the pipeline on vertex ai and the authentication process for google cloud.', 'duration': 143.248, 'highlights': ['The instruction parameter needs to be set in order to instruct the model on the task it needs to perform, such as summarizing the text in less than 50 words.', 'The pipeline for reinforcement learning from human feedback is executed on Vertex AI, running on a server on Google Cloud.', 'The process of authenticating to Google Cloud and initializing the Vertex AI Python SDK is essential for running the pipeline on Vertex AI.']}, {'end': 3110.286, 'start': 2741.43, 'title': 'Deploying ai pipeline in europe west 4', 'summary': 'Details the deployment of a reinforcement learning from human feedback pipeline in the europe west 4 region using ai platform, including creating and running the pipeline job, and discusses the strategies for evaluating large language models, mentioning the time taken for a job on the full giant reddit data set and the importance of training curves and side-by-side evaluation.', 'duration': 368.856, 'highlights': ['Creating and running the AI pipeline job The process of creating and running a reinforcement learning from human feedback pipeline job is detailed, mentioning the display name, staging bucket, template path, and parameter values, with a note on the time and hardware requirements for running the job.', 'Strategies for evaluating large language models The strategies for evaluating large language models are discussed, emphasizing the importance of training curves, side-by-side evaluation, and the time taken for a job on the full giant Reddit data set.', 'Importing and initializing the vertex AI python SDK The necessity of importing and initializing the vertex AI python SDK, with a note on the required pip installation for running in a different environment.']}], 'duration': 512.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k2598182.jpg', 'highlights': ['The process of creating and running a reinforcement learning from human feedback pipeline job is detailed, including the display name, staging bucket, template path, and parameter values.', 'The strategies for evaluating large language models are discussed, emphasizing the importance of training curves, side-by-side evaluation, and the time taken for a job on the full giant Reddit data set.', 'The instruction parameter needs to be set to instruct the model on the task it needs to perform, such as summarizing the text in less than 50 words.', 'The pipeline for reinforcement learning from human feedback is executed on Vertex AI, running on a server on Google Cloud.', 'The process of authenticating to Google Cloud and initializing the Vertex AI Python SDK is essential for running the pipeline on Vertex AI.', 'Importing and initializing the Vertex AI Python SDK is necessary, with a note on the required pip installation for running in a different environment.']}, {'end': 3411.609, 'segs': [{'end': 3139.811, 'src': 'embed', 'start': 3110.586, 'weight': 2, 'content': [{'end': 3115.391, 'text': 'It simply tells you how close the generated text is to some reference text.', 'start': 3110.586, 'duration': 4.805}, {'end': 3123.8, 'text': 'So some research has even shown that the more severely we optimize for rouge, the worse the model performance is in the case of RLHF.', 'start': 3115.832, 'duration': 7.968}, {'end': 3127.664, 'text': "So we're going to start by taking a look at some of the training curves.", 'start': 3124.561, 'duration': 3.103}, {'end': 3135.368, 'text': 'The Vertex AI RLHF pipeline that we created in the previous lesson outputs some training curves to TensorBoard.', 'start': 3128.083, 'duration': 7.285}, {'end': 3139.811, 'text': 'TensorBoard is an open source project for machine learning experiment visualization.', 'start': 3135.608, 'duration': 4.203}], 'summary': 'Optimizing for rouge can worsen model performance in rlhf. vertex ai outputs training curves to tensorboard.', 'duration': 29.225, 'max_score': 3110.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3110586.jpg'}, {'end': 3366.896, 'src': 'embed', 'start': 3330.227, 'weight': 0, 'content': [{'end': 3332.589, 'text': 'You can see this KL loss continues to increase.', 'start': 3330.227, 'duration': 2.362}, {'end': 3334.391, 'text': 'And at some point, it sort of plateaus.', 'start': 3332.709, 'duration': 1.682}, {'end': 3335.992, 'text': 'And the same thing for the reward.', 'start': 3334.771, 'duration': 1.221}, {'end': 3340.156, 'text': 'The reward keeps climbing higher and higher until at some point, it plateaus.', 'start': 3336.112, 'duration': 4.044}, {'end': 3345.321, 'text': "But we're not really seeing that for either the KL loss or the reward here in these TensorBoard files.", 'start': 3340.376, 'duration': 4.945}, {'end': 3349.085, 'text': "And so that's a pretty good indication that your model isn't really learning.", 'start': 3345.561, 'duration': 3.524}, {'end': 3352.008, 'text': "In fact, in this case it kind of seems like it's underfitting,", 'start': 3349.365, 'duration': 2.643}, {'end': 3356.913, 'text': "because there's no real trend here in either the curves from the KL loss and the reward.", 'start': 3352.008, 'duration': 4.905}, {'end': 3360.314, 'text': "But in this particular case, that wasn't too surprising.", 'start': 3357.273, 'duration': 3.041}, {'end': 3366.896, 'text': 'These were log files I pulled from tuning the model on a small subset, around 1% of the total data set.', 'start': 3360.794, 'duration': 6.102}], 'summary': 'Model shows underfitting in kl loss and reward curves on 1% subset.', 'duration': 36.669, 'max_score': 3330.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3330227.jpg'}], 'start': 3110.586, 'title': 'Tensorboard training curves analysis', 'summary': "Provides an analysis of training curves using tensorboard, highlighting the model's performance and learning behavior, revealing the impact of training data size on the model's convergence and stability.", 'chapters': [{'end': 3411.609, 'start': 3110.586, 'title': 'Tensorboard training curves analysis', 'summary': "Provides an analysis of training curves using tensorboard, highlighting the model's performance and learning behavior, revealing the impact of training data size on the model's convergence and stability.", 'duration': 301.023, 'highlights': ['The KL loss and reward curves for the RLHF model trained on a small subset of the total dataset show erratic behavior, indicating underfitting and lack of model learning.', 'Training the model on the full dataset results in KL loss and reward curves showing a more expected behavior, with both increasing and eventually stabilizing, indicating improved model learning and convergence.', 'The chapter emphasizes the importance of analyzing training curves to assess model performance and learning behavior, indicating that optimizing for rouge may negatively impact model performance.']}], 'duration': 301.023, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3110586.jpg', 'highlights': ['Training the model on the full dataset results in KL loss and reward curves showing a more expected behavior, with both increasing and eventually stabilizing, indicating improved model learning and convergence.', 'The KL loss and reward curves for the RLHF model trained on a small subset of the total dataset show erratic behavior, indicating underfitting and lack of model learning.', 'The chapter emphasizes the importance of analyzing training curves to assess model performance and learning behavior, indicating that optimizing for rouge may negatively impact model performance.']}, {'end': 3796.133, 'segs': [{'end': 3439.36, 'src': 'embed', 'start': 3411.889, 'weight': 0, 'content': [{'end': 3417.971, 'text': 'So these were some training curves that were generated from a large scale tuning job run by my teammate Bethany.', 'start': 3411.889, 'duration': 6.082}, {'end': 3422.913, 'text': 'She actually ran a bunch of experiments with this Reddit data set and the Lama 2 model.', 'start': 3418.412, 'duration': 4.501}, {'end': 3427.175, 'text': 'And I can show you what parameters she used specifically to achieve these results.', 'start': 3423.133, 'duration': 4.042}, {'end': 3432.377, 'text': 'So here is the dictionary parameter values that we created in the previous lesson.', 'start': 3427.535, 'duration': 4.842}, {'end': 3439.36, 'text': 'And, for starters, for the preference data set, the prompt data set and the evaluation data set,', 'start': 3433.198, 'duration': 6.162}], 'summary': 'Training curves from large scale tuning job with reddit data and lama 2 model.', 'duration': 27.471, 'max_score': 3411.889, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3411889.jpg'}, {'end': 3505.323, 'src': 'embed', 'start': 3456.334, 'weight': 1, 'content': [{'end': 3466.264, 'text': 'She fine tuned the Lama 2 model and the reward model train steps were set to 10, 000 as well as the reinforcement learning train steps.', 'start': 3456.334, 'duration': 9.93}, {'end': 3476.701, 'text': 'reward model learning rate multiplier was 1.0 and the reinforcement learning rate multiplier was 0.2.', 'start': 3467.509, 'duration': 9.192}, {'end': 3484.067, 'text': 'The KL coefficient was set to the default of 0.1, and the instruction was the same as before, summarize in less than 50 words.', 'start': 3476.701, 'duration': 7.366}, {'end': 3489.892, 'text': 'So now let me show you how you can access these TensorBoard files for yourself and your own projects.', 'start': 3484.368, 'duration': 5.524}, {'end': 3495.957, 'text': "So currently, we've just been interacting with Google Cloud in a notebook via the Python SDK.", 'start': 3490.253, 'duration': 5.704}, {'end': 3503.003, 'text': 'But if you go to console.cloud.google.com and go to your Google Cloud project Under the vertex AI section,', 'start': 3496.117, 'duration': 6.886}, {'end': 3505.323, 'text': "you'll see a little button that says pipelines.", 'start': 3503.003, 'duration': 2.32}], 'summary': 'Fine-tuned lama 2 model with 10,000 reward model train steps and 0.2 reinforcement learning train steps. demonstrated accessing tensorboard files via google cloud console.', 'duration': 48.989, 'max_score': 3456.334, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3456334.jpg'}, {'end': 3589.848, 'src': 'embed', 'start': 3564.522, 'weight': 5, 'content': [{'end': 3574.25, 'text': 'If you want to find the specific file within that directory yourself, you should see something called events out tfevents that will end in W110V2.', 'start': 3564.522, 'duration': 9.728}, {'end': 3579.435, 'text': "But that's how you find your specific TensorBoard logs for the reward model trainer.", 'start': 3574.43, 'duration': 5.005}, {'end': 3582.298, 'text': "For the reinforcement learning loop, it's pretty similar.", 'start': 3579.775, 'duration': 2.523}, {'end': 3589.848, 'text': "You'll just click on the reinforcer component and then open up the corresponding TensorBoard metrics artifact that is produced.", 'start': 3582.338, 'duration': 7.51}], 'summary': 'Find specific tensorboard logs for reward model trainer and reinforcement learning loop by locating events ending in w110v2 and opening corresponding artifacts.', 'duration': 25.326, 'max_score': 3564.522, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3564522.jpg'}, {'end': 3634.795, 'src': 'embed', 'start': 3604.193, 'weight': 6, 'content': [{'end': 3606.875, 'text': 'But, at the end of the day, with these large language models,', 'start': 3604.193, 'duration': 2.682}, {'end': 3613.199, 'text': 'sometimes the best way to evaluate them is just to look at the completions that they produce for a set of input prompts.', 'start': 3606.875, 'duration': 6.324}, {'end': 3621.064, 'text': 'So you might remember that in the previous lesson when we created our pipeline job, we passed in an evaluation data set.', 'start': 3613.58, 'duration': 7.484}, {'end': 3625.968, 'text': 'This is a data set of prompts, no completions, just summarization prompts.', 'start': 3621.345, 'duration': 4.623}, {'end': 3634.795, 'text': "We're calling this an evaluation dataset, but it might differ from how you are used to using evaluation datasets with machine learning in the past.", 'start': 3626.508, 'duration': 8.287}], 'summary': 'Evaluating large language models by looking at completions for input prompts.', 'duration': 30.602, 'max_score': 3604.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3604193.jpg'}], 'start': 3411.889, 'title': 'Training and evaluating large scale models', 'summary': 'Discusses training curves from a large scale tuning job with reddit data set and lama 2 model, including specific parameters used. it also demonstrates accessing tensorboard files for visualization and evaluating model performance using a dataset of prompts and completions, with a focus on analyzing specific logs and evaluation results.', 'chapters': [{'end': 3484.067, 'start': 3411.889, 'title': 'Training curves for large scale tuning job', 'summary': 'Discusses the training curves generated from a large scale tuning job with reddit data set and lama 2 model, showcasing specific parameters used to achieve results such as training on the full data set, fine-tuning the model, and setting train steps and learning rate multipliers for both reward model and reinforcement learning.', 'duration': 72.178, 'highlights': ['My teammate Bethany ran a large scale tuning job with Reddit data set and Lama 2 model, training on the full data set instead of a subsampled version.', 'The reward model train steps and reinforcement learning train steps were both set to 10,000.', 'The reward model learning rate multiplier was set to 1.0 and the reinforcement learning rate multiplier was set to 0.2.', 'The KL coefficient was set to the default value of 0.1.']}, {'end': 3796.133, 'start': 3484.368, 'title': 'Accessing tensorboard files and evaluating model performance', 'summary': 'Demonstrates how to access tensorboard files for visualization and how to evaluate model performance using a dataset of prompts and completions, with a focus on accessing and analyzing specific tensorboard logs and evaluation results.', 'duration': 311.765, 'highlights': ['The chapter demonstrates how to access TensorBoard files for visualization and evaluation of model performance. The chapter provides a step-by-step guide on accessing TensorBoard files for visualization and evaluation of model performance within the Google Cloud platform.', 'Accessing specific TensorBoard logs for reward model trainer and reinforcement learning loop. Detailed instructions are given for accessing specific TensorBoard logs for the reward model trainer and reinforcement learning loop, including locating the logs within Google Cloud Storage and understanding the visualization of training curves.', 'Evaluating model performance using a dataset of prompts and completions. The chapter explains the process of evaluating model performance using a dataset of prompts and completions, emphasizing the use of a dataset for bulk inference jobs and the generation of completion results for analysis.']}], 'duration': 384.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3411889.jpg', 'highlights': ['My teammate Bethany ran a large scale tuning job with Reddit data set and Lama 2 model, training on the full data set instead of a subsampled version.', 'The reward model train steps and reinforcement learning train steps were both set to 10,000.', 'The reward model learning rate multiplier was set to 1.0 and the reinforcement learning rate multiplier was set to 0.2.', 'The KL coefficient was set to the default value of 0.1.', 'The chapter demonstrates how to access TensorBoard files for visualization and evaluation of model performance.', 'Accessing specific TensorBoard logs for reward model trainer and reinforcement learning loop.', 'Evaluating model performance using a dataset of prompts and completions.']}, {'end': 4368.153, 'segs': [{'end': 3871.68, 'src': 'embed', 'start': 3842.887, 'weight': 2, 'content': [{'end': 3847.708, 'text': 'We have a data set that has results from the tuned LLAMA2 model.', 'start': 3842.887, 'duration': 4.821}, {'end': 3851.649, 'text': 'And then we have a data set that has results from the untuned LLAMA2 model.', 'start': 3847.848, 'duration': 3.801}, {'end': 3860.534, 'text': "If we look at the first example in this untuned data set, what you'll see is that the prompt is the same, but the completion is going to be different,", 'start': 3851.989, 'duration': 8.545}, {'end': 3864.676, 'text': 'because it came from the model before we ran our RLHF tuning job.', 'start': 3860.534, 'duration': 4.142}, {'end': 3871.68, 'text': "You can see that it's the same prompt as before about Valentine's Day and roses, but the prediction is different.", 'start': 3865.156, 'duration': 6.524}], 'summary': 'Comparison of tuned and untuned llama2 models reveals differing predictions for the same prompt.', 'duration': 28.793, 'max_score': 3842.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3842887.jpg'}, {'end': 3907.61, 'src': 'embed', 'start': 3882.665, 'weight': 0, 'content': [{'end': 3889.626, 'text': 'So if we scroll back up to the completion produced by the tuned model, you can see that it is in fact different.', 'start': 3882.665, 'duration': 6.961}, {'end': 3895.107, 'text': 'And one difference you might notice is that the tuned model produced a summary in first person.', 'start': 3889.866, 'duration': 5.241}, {'end': 3899.308, 'text': 'So in the same voice as the original Reddit poster,', 'start': 3895.267, 'duration': 4.041}, {'end': 3907.61, 'text': 'while the untuned model refers to the author instead of saying it in the same voice as the person posting on Reddit.', 'start': 3899.308, 'duration': 8.302}], 'summary': 'Tuned model produces summary in first person, while untuned model refers to the author.', 'duration': 24.945, 'max_score': 3882.665, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3882665.jpg'}, {'end': 4191.113, 'src': 'embed', 'start': 4156.993, 'weight': 5, 'content': [{'end': 4163.118, 'text': "Now, if you're wondering, for your own RLHF tuning jobs, how do you access the batch evaluation results??", 'start': 4156.993, 'duration': 6.125}, {'end': 4167.683, 'text': "Well, you'll do this again by going into the Cloud Console and opening up your pipeline.", 'start': 4163.578, 'duration': 4.105}, {'end': 4172.407, 'text': 'But this time, you will zoom in on the component that says Perform Inference.', 'start': 4168.023, 'duration': 4.384}, {'end': 4176.127, 'text': "Under perform inference, you'll see a component called bulk infer.", 'start': 4172.947, 'duration': 3.18}, {'end': 4180.35, 'text': 'This is the component that just performs a bulk inference job,', 'start': 4176.348, 'duration': 4.002}, {'end': 4191.113, 'text': 'meaning it takes in our JSONL file of prompts in our evaluation data set and then calls the model to produce completions for each one of those prompts.', 'start': 4180.35, 'duration': 10.763}], 'summary': 'Access batch evaluation results by navigating to perform inference and using bulk infer component in cloud console.', 'duration': 34.12, 'max_score': 4156.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k4156993.jpg'}, {'end': 4271.278, 'src': 'embed', 'start': 4224.934, 'weight': 3, 'content': [{'end': 4232.819, 'text': 'This is a really interesting technique where we are actually creating preference datasets that are labeled by an off-the-shelf large language model.', 'start': 4224.934, 'duration': 7.885}, {'end': 4238.722, 'text': 'So previously, when we looked at the preference dataset, it was labeled by human labelers,', 'start': 4233.359, 'duration': 5.363}, {'end': 4246.285, 'text': "but actually in the research area they're now looking at different ways to use a large language model to actually create that preference dataset.", 'start': 4238.722, 'duration': 7.563}, {'end': 4256.07, 'text': "So this is a pretty interesting paper that I would recommend taking a look at if you're curious to see how we might use an AI model to help generate a preference data set.", 'start': 4246.706, 'duration': 9.364}, {'end': 4263.334, 'text': 'And then similarly in the topic of using LLMs to help us in this RLHF process.', 'start': 4256.43, 'duration': 6.904}, {'end': 4271.278, 'text': 'another interesting technique is called auto side-by-side, and this is where you perform side-by-side evaluation, like we did in the notebook,', 'start': 4263.334, 'duration': 7.944}], 'summary': 'Research explores using large language models to label preference datasets, potentially changing the way ai generates preference data.', 'duration': 46.344, 'max_score': 4224.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k4224934.jpg'}, {'end': 4361.087, 'src': 'embed', 'start': 4331.212, 'weight': 6, 'content': [{'end': 4335.735, 'text': 'So that wraps up our lesson on evaluating the results from RLHF.', 'start': 4331.212, 'duration': 4.523}, {'end': 4341.58, 'text': "So I'll see you in the next video where we will conclude the course and wrap up everything that we've learned.", 'start': 4335.956, 'duration': 5.624}, {'end': 4346.996, 'text': 'Congratulations on finishing the short course on RLHF.', 'start': 4343.513, 'duration': 3.483}, {'end': 4353.821, 'text': 'We started off with a conceptual overview of how RLHF works and the different data sets involved.', 'start': 4347.636, 'duration': 6.185}, {'end': 4361.087, 'text': 'Then you saw how to tune the OSS LAMA2 model using an ML pipeline and how to evaluate the results.', 'start': 4354.382, 'duration': 6.705}], 'summary': 'Course concluded on rlhf with overview, model tuning, and result evaluation.', 'duration': 29.875, 'max_score': 4331.212, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k4331212.jpg'}], 'start': 3796.494, 'title': 'Model tuning and rlhf evaluation', 'summary': 'Compares the impact of tuning on the lama 2 model, highlighting the shift in voice and content of the generated summaries. it also introduces new techniques in rlhf and demonstrates the comparison between completions from untuned and tuned models.', 'chapters': [{'end': 3907.61, 'start': 3796.494, 'title': 'Lama 2 model tuning analysis', 'summary': 'Demonstrates the impact of tuning on the lama 2 model, comparing completion results before and after the tuning job, showing a shift in the voice and content of the generated summaries.', 'duration': 111.116, 'highlights': ["The tuned model produced a summary in first person, aligning with the original Reddit poster, while the untuned model did not. This highlights the shift in voice between the tuned and untuned models, showcasing the impact of tuning on the model's output.", "The completion from the untuned model differs from the tuned model, indicating the influence of the tuning process on the model's output. This emphasizes the tangible impact of the tuning job on the content and structure of the model's generated completions.", "Results from the tuned LLAMA2 model showcase differences compared to the untuned LLAMA2 model, demonstrating the impact of tuning on completion variations. This underlines the observable differences between the tuned and untuned models, providing clear evidence of the tuning's effect on the model's output."]}, {'end': 4368.153, 'start': 3907.93, 'title': 'Evaluating rlhf results', 'summary': 'Demonstrates how to compare completions from untuned and tuned models through creating a data frame, and introduces new techniques in rlhf such as rlaif and auto side-by-side evaluation.', 'duration': 460.223, 'highlights': ['The chapter demonstrates the process of comparing completions from untuned and tuned models by creating a data frame with prompts, completions from untuned model, and completions from tuned model.', 'The chapter introduces the RLAIF technique, which involves creating preference datasets labeled by a large language model, offering a new approach to generate preference data sets.', 'The chapter discusses the auto side-by-side technique, where a third large language model is used to determine and explain the preferred completion between the untuned and tuned models, showcasing an innovative approach in RLHF.', 'The chapter provides guidance on accessing batch evaluation results for RLHF tuning jobs through the Cloud Console, specifically utilizing the bulk infer component to generate completions for prompts in the evaluation data set.', 'The chapter concludes the lesson on evaluating RLHF results and expresses excitement for the future application of RLHF tuning in creating innovative models.']}], 'duration': 571.659, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/MrIUl6TUV6k/pics/MrIUl6TUV6k3796494.jpg', 'highlights': ['The tuned model produced a summary in first person, aligning with the original Reddit poster, while the untuned model did not.', "The completion from the untuned model differs from the tuned model, indicating the influence of the tuning process on the model's output.", 'Results from the tuned LLAMA2 model showcase differences compared to the untuned LLAMA2 model, demonstrating the impact of tuning on completion variations.', 'The chapter introduces the RLAIF technique, which involves creating preference datasets labeled by a large language model, offering a new approach to generate preference data sets.', 'The chapter discusses the auto side-by-side technique, where a third large language model is used to determine and explain the preferred completion between the untuned and tuned models, showcasing an innovative approach in RLHF.', 'The chapter provides guidance on accessing batch evaluation results for RLHF tuning jobs through the Cloud Console, specifically utilizing the bulk infer component to generate completions for prompts in the evaluation data set.', 'The chapter concludes the lesson on evaluating RLHF results and expresses excitement for the future application of RLHF tuning in creating innovative models.']}], 'highlights': ["RLHF is crucial in aligning LLM's output with human preferences and values, serving as an important tuning technique.", 'The flexibility of natural language and the existence of multiple valid summaries make it challenging to find the best summary for a piece of text.', "The preference data list contains examples with prompts ending with 'summary colon', indicating the specific formatting required for the dataset and its importance for matching expected production traffic during training.", 'The RLHF pipeline involves creating preference and prompt datasets, training reward and base language models, and executing reinforcement learning loops.', 'The value of large model reference specifies which large language model to tune, with options including LAMA27B, TextBison, and the T5X family of models.', 'The process of creating and running a reinforcement learning from human feedback pipeline job is detailed, including the display name, staging bucket, template path, and parameter values.', 'Training the model on the full dataset results in KL loss and reward curves showing a more expected behavior, with both increasing and eventually stabilizing, indicating improved model learning and convergence.', 'My teammate Bethany ran a large scale tuning job with Reddit data set and Lama 2 model, training on the full data set instead of a subsampled version.', 'The tuned model produced a summary in first person, aligning with the original Reddit poster, while the untuned model did not.']}