opt-350m
Maintainer: facebook
115
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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 opt-350m model is part of the Open Pre-trained Transformers (OPT) suite of decoder-only pre-trained transformer language models ranging from 125M to 175B parameters, developed and released by Meta AI. The goal of the OPT models is to enable reproducible and responsible research at scale by making these large language models fully and responsibly available to the research community.
The opt-350m model was predominantly pre-trained on English text, with a small amount of non-English data present in the training corpus via CommonCrawl. It was trained using a causal language modeling (CLM) objective, making it part of the same family of decoder-only models as GPT-3. Like GPT-3, the opt-350m model was pre-trained using the self-supervised causal language modeling objective.
Similar OPT models include the opt-1.3b, opt-30b, and opt-66b models, all of which were developed and released by Meta AI.
Model inputs and outputs
The opt-350m model takes text as input and generates text as output. It can be used for a variety of natural language processing tasks such as text generation, summarization, and question answering.
Inputs
- Text prompt
Outputs
- Generated text continuation of the input prompt
Capabilities
The opt-350m model is capable of generating coherent and contextually relevant text given an input prompt. It can be used to produce long-form content such as articles, stories, or dialogues. Additionally, the model can be fine-tuned on specific tasks or datasets to enhance its performance in those domains.
What can I use it for?
The opt-350m model can be used for a variety of text-generation tasks, such as:
- Content creation: Generating articles, stories, or other long-form text
- Dialogue systems: Building chatbots or conversational agents
- Summarization: Condensing longer text into concise summaries
- Question answering: Providing informative responses to questions
Additionally, the model can be fine-tuned on specific tasks or datasets to improve its performance in those areas. For example, the model could be fine-tuned on a dataset of technical documents to generate technical reports or manuals.
Things to try
One interesting thing to try with the opt-350m model is to provide it with prompts that explore its biases and limitations. The model's training data contains a lot of unfiltered content from the internet, which can lead to biased and potentially harmful text generation. By experimenting with prompts that touch on sensitive topics, you can gain insights into the model's shortcomings and work towards developing more responsible and ethical large language models.
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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