A Comprehensive Introduction to Large Language Models
0:00:05 Introduction to LLMs: The video aims to provide mental models for understanding large language models (LLMs) like ChatGPT, covering their strengths, weaknesses, and potential pitfalls.
0:00:43 Building LLMs: Pre-training Stage: Building LLMs involves multiple stages, starting with pre-training, where the model is trained on vast amounts of text from the internet.
0:01:13 Data Diversity and Quality: This stage involves acquiring a large, diverse set of high-quality documents from publicly available sources, as demonstrated by the FineWeb dataset curated by Hugging Face.
0:02:47 Common Crawl & Data Filtering: The initial data often comes from Common Crawl, which requires filtering through URL blocklists, extracting text from HTML, language detection, and removing personally identifiable information (PII).
0:07:48 Tokenization: Representing text for neural networks involves converting it into a one-dimensional sequence of tokens (symbols), with a tradeoff between vocabulary size and sequence length.
0:11:01 Byte Pair Encoding (BPE): Compressing token sequences uses BPE, which looks for consecutive bytes/symbols that are very common (e.g., GPT-4 uses 100,277 symbols).
0:11:45 Neural Network Training: Neural networks model the statistical relationships between tokens by predicting the next token in a sequence using a limited context window (e.g., 8,000 tokens).
0:15:21 Input and Output of Neural Network: Inputs are token sequences; outputs are probability distributions over the vocabulary, representing the likelihood of each token appearing next.
0:20:17 Neural Network Internals: The neural network mixes inputs with parameters (weights) to make predictions. Modern networks have billions of parameters, initially random, adjusted during training.
0:20:50 Transformer Architecture: A transformer architecture has different layers like the "attention block" and "multi-layer perceptron block".
0:26:11 Inference (Generating New Data): Inference involves starting with a few tokens and sampling the next token based on the network's probability distribution.
0:31:03 Exploring tokenization and data preparation through a GPT2 reproduction.
0:33:05 Cost reductions in LLM training due to better data sets and faster computers.
0:34:51 The process to train a GPT2 model where every single line is an update to the model and improving 1 million tokens in the training set simultaneously.
0:35:57 Loss as a tool to measure the improvement in the predictions.
0:36:32 Compute infrastructure needed for the models, with a large number of GPU's needed to train in the cloud and the compute cost associated to this.
0:41:02 Gold rush for GPU's drove the Nvidia stock price to 3.4 Trillion dollar mark.
0:42:48 Trained and released meta model with it's different weights that can be applied to code.
0:43:50 Limitation of the data set from being useful, with a need to transfer over questions and solve problems.
0:44:59 Tokenizing text is a very small finite computer to be processed and a computer code can fix that.
0:46:00 How to run GPT2 from this point forward in two weeks and the lack of coherence from the lack of token.
0:54:46 Neural networks use tokens to process information, so everything after is in that format.
1:00:51 The role of the pre training and post training in the model, creating a good assistant to interact with.
1:02:05 The importance to keep in mind how many different models are implemented with a lot of code, with high volume numbers and data from the data sets.
1:10:05 The need for human data collectors to form and gather this dataset, with the need for code implementations and more diverse data sets.
1:11:21 Model hallucinations as a problem, due to the random outputs that they can give at random, a specific implementation of a model will need to be done to make sure there are no issues like that present.
1:14:39 In prebuilt model the memory has everything hard coded and there's no actual memory being used, making the model weaker.
1:16:50 The use of memory to keep the quality higher for the data and to make it work correctly, with data sets being larger.
1:17:01 The cost for the models to preform well during inference and how it was solved using various implementations.
1:19:35 The role of a neuron and how it lights up when asked about Bible verses, making it all be linked to the verses from the pretraining stage.
1:20:07 With all these connections from the various data points, what happens when models are mixed.
1:20:41 Making the bots work properly by checking their responses and what they know, a huge role in this can be played with external tools to assist.
1:33:50 If the data from a search came back and said wrong answer in the URL, a huge conflict has already occurred.
1:36:17 The goal is not just the final solution, but the step by step instructions that helped the model come up with that conclusion.
1:37:01 An 80 percent of time model should not be trusted fully, with the user having all the responsibility.
1:38:30 The importance of the user to code properly and with a great plan, and to also look what tools are available.
1:40:04 A lot of people will look for shortcuts and will end up costing the project, but it has to be a more calculated step by step, to gain value and not make mistakes.
1:41:35 A data scientist at the end of the day does't know which token is wrong and will need to correct it, with their job being the quality control.
1:45:15 It will take multiple GPUs to solve the answer, and with each GPU having a little data to be combined.
1:47:03 A recommendation was to get the tool set from YouTube, where you can gain assistance from watching those videos if the tools are lacking info.
1:47:34 This process has been around for a very long period of time but not as popular, and now it's coming into a new era where it will improve immensely from now to the following future due to better data and tools.
1:48:30 As far as to the long time cost, this project is just one and the beginning for new models and is important and will stay that way.
1:55:00 Data mining and data mining have become more critical now, and as of the cost more processes have been added for the data, with more jobs requiring humans to clean the data and keep models running.
1:55:54 The importance of multi-tonality due to the fact that it makes the bots more capable to connect and also not be very difficult for someone to do this.
2:00:51 Another key topic is that the models have a huge range with the data, and each one can use another tool to help them, with a key to be the best to achieve the goals.
2:01:14 Humans will use those tools to find the best way to learn and find the best way to understand for a longer period of time with great quality and also not have any issues.
2:04:41 Model isn't a real person, it's almost an emulation and a code that needs to be corrected as it runs as the user requests.
2:07:34 The website to check for a quality of a certain weight and to also inspect data by the team.
2:10:55 Open weights being a great tool for open source models and the good things that it provides.
2:14:24 AI news as a good list to keep up with news about the bot's and a great place to find solutions.
2:14:44 AI twitter accounts will help in those updates, but has to be taken with a grain of salt and do personal research before coming to any solutions.
2:16:01 Open AI website for GPT 3.0 and 4.0, Google AI, and meta with the Lama model.
2:18:20 The ability for the models to know some of the information and what it does will make it work to the best of it's capabilities and how to control that.
2:21:59 Deep dive into training that was done to see the patterns and to see what data was working the best.
2:22:55 The way to fix this is that after the data set and code was implemented, the model will still need to be fixed.
2:23:23 Hallucination is going to increase and keep giving bad results, what are the proper results to check before submitting?
2:26:50 It's important to know all the different algorithms and functions needed to reach those goals.
2:29:01 What happens when you use the data to preform well with other functions?
2:29:37 Human and open data will work more closely in order to keep improving in all the cases, as well as get the data and model in a better area to be trusted.
2:30:06 There still going to be humans and bots working in a close relationship for many years in order to keep improving the speed and quality.
2:30:30 It is always good to do research on the topic before using the website or model.
2:32:33 If the model or model's aren't giving the solution that's been requested then do not use them and do proper research.
2:33:14 Make sure to note all the issues that you have found, the pros and cons need to weight higher than cons for it to be working for you!