gpt2-medium
Maintainer: openai-community
126
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The gpt2-medium model is a 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. It was developed by the OpenAI team, as detailed in the associated research paper and GitHub repo. The model is a medium-sized version of the GPT-2 family, with the GPT2, GPT2-Large and GPT2-XL models being larger in size.
Model inputs and outputs
Inputs
- Text prompts of up to 1024 tokens
Outputs
- Continued text generation based on the provided prompt
Capabilities
The gpt2-medium model can be used to generate human-like text continuations based on the given prompt. It exhibits strong language understanding and generation capabilities, allowing it to be used for a variety of natural language tasks such as writing assistance, creative writing, and chatbot applications.
What can I use it for?
The gpt2-medium model can be used for a variety of text generation tasks, such as:
- Writing Assistance: The model can be used to provide autocompletion and grammar assistance for normal prose or code.
- Creative Writing: The model can be used to explore the generation of creative, fictional texts and aid in the creation of poetry and other literary works.
- Entertainment: The model can be used to create games, chatbots, and generate amusing text.
However, users should be aware of the model's limitations and biases, as detailed in the OpenAI model card. The model does not distinguish fact from fiction and reflects the biases present in its training data, so it should be used with caution, especially in applications that interact with humans.
Things to try
One interesting aspect of the gpt2-medium model is its ability to capture long-range dependencies in text, allowing it to generate coherent and contextually-relevant continuations. Try providing the model with a prompt that sets up an interesting scenario or narrative, and see how it develops the story in creative and unexpected ways. You can also experiment with adjusting the generation parameters, such as temperature and top-k/top-p sampling, to explore different styles of text generation.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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