A language model is a system that knows how to predict the next word of a sentence or fill in the missing words of a sentence.
A language model predicts the next word or token in a sentence, while a tokenizer breaks down a sentence into individual tokens (words or sub-word units).
Pre-training is the first step in language model training, where the model is trained to predict the next word in a sentence using a large dataset, such as Wikipedia. This helps the model learn about the world and various concepts.
GPT-4 was not explicitly trained to give correct answers. It was initially trained to give most likely next words, and later stages focused on giving correct answers, but the process was not perfect due to various factors such as incorrect or misleading information on the internet and users preferring more confident answers over correct ones.
Users can help GPT-4 provide high-quality information by giving it custom instructions that prime it to give good answers, focusing on the kinds of things in a document that would suggest high-quality information. This is done by prepending custom instructions to all queries.
GPT-4 and other language models can't provide accurate information about themselves, as they don't know how they were trained or what Transformer architecture they are based on. They also struggle with understanding URLs, handling time periods after their knowledge cut-off (September 2021), and solving logic puzzles that require reasoning and logic outside their usual patterns.
The GOAT analogy is used to refer to individuals or groups with exceptional skills and achievements in a particular field. In the video transcript, Michael Jordan is referred to as the GOAT in basketball, while Elvis Presley and The Beatles are referred to as the GOATs in music due to their profound influence and achievements.
In a multi-stage conversation, the entire conversation is passed back to the language model, and it is told what it told you in the previous stage. This allows the model to maintain context and invent a conversation in which it said something different, as demonstrated in the video transcript with the analogy of money being like kangaroos.
When using OpenAI's API for language models, users should be aware of their usage to avoid spending too much money and hitting API limits, especially during the first 48 hours of account creation. The initial limits are three requests per minute for free and paid users. Users should also create a function to handle rate limits and keep an eye on their usage.
B float 16 is a 16-bit floating point format that is useful on recent Nvidia GPUs. It takes twice as much RAM as 8-bit, but it can significantly reduce the time taken for the model to complete sentences.
Gptq is a different kind of discretization that optimizes a model to work with lower precision data automatically. It can be faster than using 16-bit, even if it internally casts the data to 16 bit for each layer.
Retrieval augmented generation is a technique where a language model searches for documents related to a question and uses the information from those documents to generate a more accurate answer.
The Python program is called 'chat'.
The name of the language model is discretized 7B.
The alternative language model is called 'llama.cpp' and it uses a different format called 'gguf'.