gpt-neo-125m

Maintainer: EleutherAI

Total Score

163

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The gpt-neo-125m is a 125 million parameter transformer model developed by EleutherAI, a collective of AI researchers and engineers. It is a replication of the GPT-3 architecture, with the "GPT-Neo" referring to the class of models. This particular model was trained on the Pile, a large-scale curated dataset created by EleutherAI, for 300 billion tokens over 572,300 steps.

Compared to similar models, the gpt-neo-125m is a smaller and more lightweight version of GPT-Neo 2.7B and GPT-NeoX-20B, which have 2.7 billion and 20 billion parameters respectively. These larger models demonstrate improved performance on various benchmarks compared to the 125M version.

Model inputs and outputs

Inputs

  • Text prompt: The model takes in a text prompt as input, which it uses to generate the next token in a sequence.

Outputs

  • Generated text: The model outputs a sequence of generated text, continuing from the provided prompt. The generated text is produced in an autoregressive manner, with the model predicting the next token based on the previous tokens in the sequence.

Capabilities

The gpt-neo-125m model is a capable language generation model that can be used to produce human-like text from a given prompt. It has learned an internal representation of the English language that allows it to generate coherent and contextually relevant text. However, as an autoregressive model, it is best suited for tasks like text generation and may not perform as well on other NLP tasks that require more sophisticated reasoning.

What can I use it for?

The gpt-neo-125m model can be used for a variety of text generation tasks, such as creative writing, content generation, and chatbots. For example, you could use the model to generate product descriptions, short stories, or engaging dialog. The model's relatively small size also makes it suitable for deployment on resource-constrained devices or platforms.

However, it's important to note that the model was trained on a dataset that contains potentially offensive content, so the generated text may include biases, profanity, or other undesirable content. It's recommended to carefully curate and filter the model's outputs before using them in production or releasing them to end-users.

Things to try

One interesting aspect of the gpt-neo-125m model is its ability to capture and generate long-range dependencies in text. Try providing the model with a long, multi-sentence prompt and see how it continues the narrative, maintaining coherence and consistency over several paragraphs. This can showcase the model's understanding of contextual information and its capacity for generating coherent, extended passages of text.

Additionally, you can experiment with providing the model with prompts that require some level of reasoning or world knowledge, such as answering questions or completing tasks. While the model may not excel at these types of tasks out-of-the-box, observing its strengths and limitations can provide valuable insights into its capabilities and potential areas for improvement.



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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