Kaludi

chatgpt-gpt4-prompts-bart-large-cnn-samsum

Kaludi

77

The chatgpt-gpt4-prompts-bart-large-cnn-samsum model is a fine-tuned version of the philschmid/bart-large-cnn-samsum model on a dataset of ChatGPT and GPT-3 prompts. This model generates prompts that can be used to interact with ChatGPT, BingChat, and GPT-3 language models. The model was created by Kaludi, and achieves a train loss of 1.2214, validation loss of 2.7584, and was trained for 4 epochs. It uses the BART-large-cnn architecture and was fine-tuned on a dataset of high-quality ChatGPT and GPT-3 prompts. Similar models include the chatgpt-prompts-bart-long model, which is also a fine-tuned BART model for generating ChatGPT prompts, and the chatgpt-prompt-generator-v12 model, which is another BART-based prompt generator. Model inputs and outputs Inputs Text prompts to generate ChatGPT, BingChat, or GPT-3 prompts Outputs Generated text prompts that can be used to interact with large language models like ChatGPT, BingChat, or GPT-3 Capabilities The chatgpt-gpt4-prompts-bart-large-cnn-samsum model can generate unique and high-quality prompts for interacting with large language models. These prompts can be used to create personas, simulate conversations, or explore different topics and use cases. The model has been finetuned on a diverse dataset of prompts, enabling it to generate a wide variety of outputs. What can I use it for? You can use this model to quickly and easily generate prompts for interacting with ChatGPT, BingChat, or GPT-3. This can be helpful for a variety of use cases, such as: Exploring different conversational scenarios and personas Generating prompts for chatbots or conversational agents Experimenting with language model capabilities and limitations Collecting training data for other language models or applications The model is available through a Streamlit web app, making it easy to use without any additional setup. Things to try One interesting thing to try with this model is to generate prompts that explore the capabilities and limitations of large language models like ChatGPT. You could generate prompts that test the model's knowledge on specific topics, its ability to follow instructions, or its tendency to hallucinate or generate biased outputs. By carefully analyzing the responses, you can gain insights into how these models work and where they may have weaknesses. Another idea is to use the generated prompts as a starting point for more complex conversational interactions. You could take the prompts and expand on them, adding additional context or instructions to see how the language models respond. This could be a useful technique for prototyping conversational applications or exploring the boundaries of what these models can do.

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