To build another ChatGPT model, you can follow these steps:

1. Collect a large dataset of conversational text. This can be conversations from online chat rooms, social media, or any other source that contains natural language conversations.

2. Preprocess the data to clean and prepare it for training. This may involve lowercasing, removing punctuation, and splitting the text into individual words.

3. Split the data into training and validation sets. You will use the training set to train the model, and the validation set to evaluate its performance.

4. Train the ChatGPT model on the training data using a machine learning library or framework, such as TensorFlow or PyTorch. You can use the original ChatGPT model as a starting point, or you can build your own model from scratch using a transformer architecture.

5. Fine-tune the model on the validation data to improve its performance. This may involve adjusting the model's hyperparameters, such as the learning rate or the number of layers.

6. Test the model on a test dataset to evaluate its performance. You can use metrics such as perplexity or BLEU to gauge the quality of the model's output.

7. Use the trained model to generate responses to prompts or to participate in conversations.

I hope this helps! Let me know if you have any questions.

There are several ways to collect a large dataset of conversational text:

1. Scrape social media or online chat rooms: You can use web scraping tools to extract conversations from social media platforms or online chat rooms. Be sure to respect the terms of service of these platforms and obtain the necessary permissions before collecting any data.

2. Use publicly available datasets: There are several publicly available datasets that contain conversational text, such as the Cornell Movie Dialogs Corpus or the OpenSubtitles Corpus. These datasets may be sufficient for some tasks, but they may not be as diverse or representative as a dataset collected from a specific domain or task.

3. Create your own dataset: You can also create your own dataset by having people participate in conversations or by using a chatbot to generate conversational data. This can be a time-consuming process, but it can also allow you to collect data that is tailored to your specific needs.

Regardless of the method you choose, it is important to ensure that the dataset is diverse and representative of the task you want the ChatGPT model to perform.

Here are some publicly available datasets that contain conversational text:

1. Cornell Movie Dialogs Corpus: This dataset contains conversations between characters in movie scripts. It has over 220,000 conversational exchanges between 10,292 pairs of characters.

2. OpenSubtitles Corpus: This dataset contains subtitles for movies and TV shows in many languages. It has over 25 million lines of dialogue and covers a wide range of topics and genres.

3. Reddit Comments Corpus: This dataset contains over 1.7 billion comments from Reddit, a social news aggregation and discussion website. It can be used to train models for tasks such as language modeling or conversation generation.

4. Twitter Conversation Corpus: This dataset contains over 2 million conversations from Twitter, a microblogging and social networking platform. It can be used to train models for tasks such as conversation generation or sentiment analysis.

5. MELD (Movies, TV Shows, and Music Dataset): This dataset contains conversations from movies, TV shows, and music concerts, as well as associated audio and visual data. It can be used to train models for tasks such as audio-visual sentiment analysis or dialogue generation.

These are just a few examples of the many publicly available datasets that contain conversational text. It is important to carefully evaluate the suitability of any dataset for your specific task before using it to train a model.