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opt-1.3b

Maintainer: facebook

Total Score

137

Last updated 5/16/2024

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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

opt-1.3b is a large language model released by Meta AI as part of their Open Pre-trained Transformer (OPT) suite of models. Like the GPT-3 family of models, opt-1.3b is a decoder-only transformer model trained using self-supervised causal language modeling. The model was pretrained on a diverse corpus of 180B tokens, including web pages, books, and other online text.

The opt-1.3b model is one of several OPT models ranging from 125M to 175B parameters, all of which Meta AI aims to share responsibly with researchers. This open access is intended to enable more voices to study the impact and improve upon these large language models, which can exhibit biases and limitations due to the nature of their training data.

Similar OPT models include the larger opt-30b and opt-66b versions. The blip2-opt-2.7b model also leverages the OPT architecture, combining it with CLIP-like image encoding for multimodal applications.

Model inputs and outputs

Inputs

  • Text prompt: The model takes in a text prompt as input, which it uses to generate additional text in an autoregressive manner.

Outputs

  • Generated text: The model outputs a sequence of generated text, continuing from the provided prompt. The length and content of the generated text can be controlled through various sampling parameters.

Capabilities

The opt-1.3b model is capable of open-ended text generation, allowing users to explore a wide range of applications such as creative writing, chatbots, and language-based assistants. However, as with other large language models, the outputs can exhibit biases and inconsistencies due to the nature of the training data.

What can I use it for?

The opt-1.3b model can be used for a variety of language-based tasks, including:

  • Content generation: Generating blog posts, news articles, stories, and other types of text content.
  • Chatbots and conversational agents: Building conversational interfaces that can engage in natural language interactions.
  • Prompt engineering: Exploring different prompting strategies to elicit desired outputs from the model.
  • Fine-tuning: Further training the model on specific datasets or tasks to adapt its capabilities.

Researchers can also use the opt-1.3b model to study the behavior and limitations of large language models, as part of Meta AI's effort to enable responsible and reproducible research in this field.

Things to try

One interesting aspect of the opt-1.3b model is its ability to generate text that can exhibit biases and stereotypes present in its training data. By experimenting with different prompts, users can uncover these biases and explore ways to mitigate them, either through prompting strategies or further fine-tuning. This can provide valuable insights into the challenges of developing fair and inclusive language models.

Additionally, the model's open-ended text generation capabilities can be used to explore creative writing and storytelling. Users can try generating narratives, dialogues, and other imaginative content, and then analyze the model's outputs to better understand its strengths and limitations in this domain.



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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Total Score

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