mt0-xxl

Maintainer: bigscience - Last updated 5/28/2024

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Model overview

The mt0-xxl model, part of the BLOOMZ & mT0 model family, is a large language model capable of following human instructions in dozens of languages zero-shot. It was created by the BigScience workshop by finetuning the pretrained BLOOM and mT5 models on the cross-lingual task mixture dataset xP3. This process of multitask finetuning has enabled the model to generalize across a wide range of unseen tasks and languages.

Model inputs and outputs

Inputs

  • Natural language prompts expressing tasks or queries
  • The model can understand a diverse set of languages, spanning those used in the pretraining data (mc4) and finetuning dataset (xP3).

Outputs

  • Relevant, coherent text responses to the input prompts
  • The model can generate text in the languages it was trained on, allowing it to perform tasks like translation, generation, and explanation across many languages.

Capabilities

The mt0-xxl model is highly versatile, able to perform a wide variety of language tasks in multiple languages. It can translate text, summarize information, answer questions, generate creative stories, and even explain complex technical concepts. For example, it can translate a French sentence to English, write a fairy tale about a troll saving a princess, or explain backpropagation in neural networks in Telugu.

What can I use it for?

The mt0-xxl model is well-suited for applications that require multilingual natural language processing, such as chat bots, virtual assistants, and language learning tools. Its zero-shot capabilities allow it to handle tasks in languages it was not explicitly trained on, making it a valuable asset for global or multilingual projects. Companies could potentially use the model to provide customer support in multiple languages, generate content in various languages, or even assist with language learning and translation.

Things to try

One interesting aspect of the mt0-xxl model is its ability to follow instructions and perform tasks based on natural language prompts. Try providing the model with prompts that require reasoning, creativity, or cross-lingual understanding, such as asking it to write a short story about a troll saving a princess, or explaining a technical concept in a non-English language. Experiment with different levels of detail and context in the prompts to see how the model responds. You can also try the model on a variety of languages to assess its multilingual capabilities.



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Total Score

51

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The mt0-xxl-mt model is part of the BLOOMZ and mT0 family of models developed by the BigScience workshop. These models are capable of following human instructions in dozens of languages zero-shot by fine-tuning the pretrained BLOOM and mT5 multilingual language models on the xP3 crosslingual task mixture. The resulting models demonstrate strong crosslingual generalization abilities, allowing them to perform a variety of tasks in unseen languages. Model inputs and outputs Inputs Natural language prompts**: The model accepts natural language instructions and queries, such as "Translate to English: Je taime." or "Explain in a sentence in Telugu what is backpropagation in neural networks." Outputs Generated text**: The model will produce a text response based on the provided input, such as "I love you." or a sentence explaining backpropagation in Telugu. Capabilities The mt0-xxl-mt model is capable of performing a wide range of natural language tasks, including translation, question answering, summarization, and open-ended generation. It can understand and generate text in dozens of languages, making it a versatile tool for multilingual applications. What can I use it for? The mt0-xxl-mt model can be used for a variety of applications that require cross-lingual understanding and generation, such as: Multilingual customer support**: The model can be used to provide support in multiple languages, helping businesses serve a global customer base. Multilingual content creation**: The model can be used to generate high-quality content in multiple languages, facilitating the creation of localized marketing materials, website content, or educational resources. Multilingual research and collaboration**: Researchers and scientists working in international teams can use the model to bridge language barriers and facilitate knowledge sharing. Things to try One interesting aspect of the mt0-xxl-mt model is its ability to perform well on a wide range of tasks without extensive fine-tuning. Experiment with different types of prompts, such as open-ended questions, instructions, or creative writing tasks, and see how the model responds. Pay attention to the model's ability to maintain coherence and contextual understanding across multiple turns of interaction.

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