Generative AI & LLM's for Text

There are many generative AI models for text, but some of the most popular include:

GPT-3 (Generative Pre-trained Transformer 3): This model was developed by OpenAI and is one of the largest and most powerful language models in the world. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

GPT-4 (Generative Pre-trained Transformer 4): This is an advanced model released by OpenAI with additional features and capabilities. For example GPT-3 is 'unimodal', meaning it can only accept text inputs. It can process and generate various text forms, such as formal and informal language, but can't handle images or other data types. GPT-4, on the other hand, is 'multimodal'. It can accept and produce text and image inputs and outputs, making it much more diverse.

LaMDA (Language Model for Dialogue Applications): This model was developed by Google AI and is specifically designed for dialogue applications. It can generate different creative text formats of text content, like poems, code, scripts, musical pieces, email, letters, etc. It will try its best to fulfill all your requirements.

Bard: This model is also developed by Google AI and is still under development. It can generate different creative text formats of text content, like poems, code, scripts, musical pieces, email, letters, etc. It will try its best to fulfill all your requirements.

These are just a few of the many generative AI models for text that are currently available. As the field of artificial intelligence continues to develop, we can expect to see even more powerful and sophisticated generative AI models in the future.

Terminology used in Generative AI Text Models;

What are Token?

Tokens are the fundamental units of text that the model uses to process and generate language. They can represent individual characters, words, or subwords depending on the specific tokenization approach.

For example, in character-level tokenization, each individual character becomes a token. This is the simplest form of tokenization, but it can be inefficient for languages with a large number of characters, such as Chinese or Japanese.

Word-level tokenization: Word-level tokenization is more common. In this approach, each word is represented by a single token. This is more efficient than character-level tokenization, but it can be less accurate, as it does not take into account the context of words within a sentence.

Subword-level tokenization: Subword-level tokenization is a more recent approach that is gaining popularity. In this approach, words are broken down into subwords, which are then represented by tokens. This approach is more accurate than word-level tokenization, as it takes into account the context of words within a sentence.

Once the text has been tokenized, the GPT model can then process and generate language by predicting the next token in the sequence. The model is trained on a massive dataset of text and code, which allows it to learn the statistical relationships between tokens.

The number of tokens used to represent a piece of text can vary depending on the tokenization approach. For example, a word-level tokenization of the sentence "The quick brown fox jumps over the lazy dog" would consist of 13 tokens. A subword-level tokenization of the same sentence would consist of 29 tokens.

The number of tokens also affects the cost of using the GPT model. The model is charged per token, so the more tokens used, the higher the cost.

Overall, tokens are an important concept in GPT and other language models. They are the fundamental units of text that the models use to process and generate language. The number of tokens used can affect the accuracy, efficiency, and cost of using the model.

What are Training Parameters?

Training parameters are the Weights and Biases that are learned by the model during the training process. These parameters are used to represent the relationships between tokens in the model's vocabulary. The more training parameters a model has, the more complex the relationships it can represent.

Details about training parameters:

Weights: Weights are the coefficients that are used to multiply the input features. They are used to represent the strength of the relationship between each input feature and the output.

Biases: Biases are the constants that are added to the output of the model. They are used to represent the overall trend of the model's output.

Learning rate: The learning rate is a hyperparameter that controls how quickly the model learns during the training process. A higher learning rate will cause the model to learn more quickly, but it may also cause the model to overfit the training data.

Epoch: An epoch is a single pass through the entire training data. The number of epochs that a model is trained for will affect the accuracy of the model.

The number of training parameters is a measure of the complexity of a language model. Models with more training parameters are able to represent more complex relationships between tokens. However, models with more training parameters also require more data to train and are more computationally expensive to run.

The optimal number of training parameters for a language model will depend on the specific task that the model is being used for. For tasks that require a high degree of accuracy, such as machine translation, models with more training parameters may be necessary. However, for tasks that require speed or efficiency, models with fewer training parameters may be sufficient.

It is important to note that the number of training parameters is not the only factor that determines the performance of a language model. Other factors, such as the architecture of the model and the Quality of the Training Data, can also play a role.