opt-125m

Maintainer: facebook

Total Score

118

Last updated 5/19/2024

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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 opt-125m model is part of the Open Pre-trained Transformers (OPT) suite of decoder-only pre-trained transformer models ranging from 125M to 175B parameters, developed and released by Meta AI. The OPT models were trained to roughly match the performance and sizes of the GPT-3 class of models, while applying the latest best practices in data collection and efficient training. The goal is to enable reproducible and responsible research at scale, and to bring more diverse voices to the table in studying the impact of these large language models.

The opt-125m model was predominantly pretrained on English text, with a small amount of non-English data from CommonCrawl. It was trained using a causal language modeling (CLM) objective, like the GPT-3 family of models. The model can be used for prompting, evaluation of downstream tasks, and text generation. It can also be fine-tuned on specific downstream tasks using the CLM example.

Similar OPT models include the opt-350m, opt-1.3b, opt-30b, and opt-66b models, all of which share the same underlying architecture and training approach.

Model inputs and outputs

Inputs

  • Text prompts: The opt-125m model takes text prompts as input, which it then uses to generate additional text.

Outputs

  • Generated text: The model outputs generated text that continues or expands upon the input prompt.

Capabilities

The opt-125m model is capable of generating human-like text based on the provided prompt. It can be used for a variety of language-based tasks, such as creative writing, conversational interfaces, and text summarization. However, as with other large language models, the opt-125m model may exhibit biases and limitations due to the nature of its training data.

What can I use it for?

The opt-125m model can be used for a variety of language-based applications, such as:

  • Content generation: Use the model to generate text for blog posts, articles, stories, or other written content.
  • Conversational interfaces: Integrate the model into chatbots or virtual assistants to enable more natural and engaging conversations.
  • Text summarization: Fine-tune the model to summarize long-form text into concise, informative snippets.
  • Brainstorming and ideation: Use the model to generate new ideas or expand upon existing concepts.

Things to try

One interesting thing to try with the opt-125m model is to experiment with different prompting techniques. By crafting carefully worded prompts, you can encourage the model to generate text that exhibits specific stylistic or topical characteristics. Additionally, you can try fine-tuning the model on domain-specific datasets to see how its performance and outputs change for particular applications.



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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