Usage - Usage returns the number of tokens passed and generated.Ĭhoices - Message generated by the model and the status of the result. Model - Model used to generate the response. Object - Name of the API that returns the response. User - Instruction passed by the end user.Īssistant - The assistant messages help store prior responses.Īs a response Ruby OpenAI API will return an object, this object will have, System - System instruction helps set the behavior of the assistant (OpenAI response), it is the high-level instruction given for the conversation. A higher temperature value will result in more unpredictable and diverse responses and a lower temperature value will result in predictable and conservative responses. In the temperature (optional) parameter, we have to pass a value between 0 to 2. Inside the messages parameter, we should pass the role and content parameter values. In a request, We have to pass two required parameters, model and messages. We are using the gpt-3.5-turbo model as gpt-4 has only limited access at the time of writing this post. Whisper model can convert audio into text, it can perform multilingual speech recognition, speech translation, and language identification.Įmbeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text these models are useful for search, clustering, recommendations, anomaly detection, and classification tasks.Ī fine-tuned model that can detect whether text may be sensitive or unsafe, this model will check whether the passed content complies with OpenAI usage policies. All the models have max token support of 2,049 and training data up to Oct 2019.ĭavinci model is the most capable model and can do any tasks with higher quality than other models.Ĭurie model is very capable but faster and lower cost compared to the davinci model.īabbage model is capable of straightforward tasks, is very fast, and has a lower cost.Īda model is capable of very simple tasks, the very fastest model in GPT-3 model, and has the lowest cost.ĭALL-E model can generate and edit images from the description in natural language. These models were superseded by the more powerful GPT-3.5 generation models. GPT-3 models can understand and generate natural language. Text-davinci-002 model is similar capabilities to text-davinci-003 but trained with supervised fine-tuning which has max token support of 4,096 and training data up to Jun 2021.Ĭode-davinci-002 model is optimized for code completion tasks that have max token support of 8,001 and training data up to Jun 2021. It has max token support of 4,096 and training data up to Jun 2021. Text-davinci-003 model can do any language task with better quality, longer output, and consistent instruction. Gpt-3.5-turbo model is the most capable GPT-3.5 model which is optimized for chat and has max token support of 4,096 and training data up to Sept 2021. gpt-3.5-turbo is optimized for chat but works well for traditional tasks. GPT-3.5 models can understand and generate natural language or code. gpt-4-32k model has the same capability as gpt-4 model but max 32,768 tokens support and training data up to Sep 2021.gpt-4 model can do complex tasks and optimized chat it has max support for 8,192 tokens and the model is training data up to Sep 2021.GPT-4 model is great at solving complex problems with great accuracy and much more capable than the previous models, for most basic tasks there is no significant difference between GPT-4 and GPT-3.5 models. OpenAI API has various models in each version and it can be used for different use cases, These tokens are not cut up exactly where the words start or end - tokens can include trailing spaces and even sub-words. Before the API processes the prompts, the input is broken down into tokens. Tokens can be thought of as pieces of words. This is an explanation from OpenAI article, Before diving into models we have to understand what a token is.
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