bloomz
Maintainer: bigscience
491
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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 bloomz model is a family of multilingual language models trained by the BigScience workshop. It is based on the BLOOM model and fine-tuned on the cross-lingual task mixture (xP3) dataset, giving it the capability to follow human instructions in dozens of languages without additional training. The model comes in a range of sizes, from 300M to 176B parameters, allowing users to choose the appropriate size for their needs. The bloomz-mt variants are further fine-tuned on the xP3mt dataset and are recommended for prompting in non-English languages.
The bloomz model is similar to other large language models like BELLE-7B-2M, which is also based on Bloomz-7b1-mt and fine-tuned on Chinese and English data. Another related model is xlm-roberta-base, a multilingual version of RoBERTa pre-trained on 100 languages.
Model inputs and outputs
Inputs
- Prompts: The
bloomzmodel takes natural language prompts as input, which can be in any of the supported languages.
Outputs
- Generated text: The model outputs generated text that responds to the input prompt, following the instructions provided. The output can be in the same language as the input or in a different supported language.
Capabilities
The bloomz model is capable of understanding and generating text in dozens of languages, including both high-resource and low-resource languages. It can follow a wide range of instructions, such as translation, question answering, and task completion, without additional fine-tuning. This makes it a versatile tool for multilingual natural language processing tasks.
What can I use it for?
The bloomz model can be used for a variety of multilingual natural language processing tasks, such as:
- Machine translation: Use the model to translate text between different languages.
- Question answering: Ask the model questions and have it provide relevant answers.
- Task completion: Give the model instructions for a task, and have it generate the required output.
- Text generation: Use the model to generate coherent and contextually appropriate text.
The different model sizes available allow users to choose the appropriate model for their needs, balancing performance and resource requirements.
Things to try
One interesting aspect of the bloomz model is its ability to generalize across languages. Try providing prompts in different languages and observe how the model responds. You can also experiment with mixing languages within a single prompt to see how the model handles code-switching.
Additionally, the bloomz-mt variants may be particularly useful for applications where the input or output language is not English. Explore the performance of these models on non-English tasks and compare them to the original bloomz versions.
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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bloomz-7b1
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The bloomz-7b1 is a large language model developed by the BigScience research workshop. It is part of the BLOOMZ and mT0 model family, which are capable of following human instructions in dozens of languages zero-shot. The model was created by fine-tuning the BLOOM and mT5 pre-trained multilingual language models on the xP3 crosslingual task mixture dataset. This resulted in a model that can generalize to unseen tasks and languages. Model inputs and outputs The bloomz-7b1 model is a text-to-text transformer that can take natural language prompts as input and generate coherent text responses. It has been trained on a vast multilingual dataset spanning 46 natural languages and 13 programming languages. The model can understand both the languages used in pre-training as well as the additional languages introduced during fine-tuning. Inputs Natural language prompts in a variety of languages, including instructions, questions, and open-ended text generation tasks. Outputs Fluent text responses in the same languages as the input prompts, demonstrating the model's ability to understand and generate content across many languages. Capabilities The bloomz-7b1 model has shown strong zero-shot performance on a wide range of tasks, including translation, question answering, and few-shot learning. It can be prompted to perform tasks it was not explicitly trained for by framing them as text generation problems. For example, the model can be asked to "Translate to English: Je taime" and generate the response "I love you." What can I use it for? The bloomz-7b1 model is well-suited for research and exploration of large language models, particularly in the areas of multilingual and crosslingual learning. Developers and researchers can use the model as a foundation for building applications that require natural language understanding and generation in multiple languages. Some potential use cases include: Building multilingual chatbots and virtual assistants Developing crosslingual information retrieval and question answering systems Exploring the capabilities and limitations of zero-shot learning in language models Things to try One interesting aspect of the bloomz-7b1 model is its ability to understand and generate text in dozens of languages. Experiment with prompting the model in different languages to see how it responds. You can also try providing the model with more context about the desired language or task, such as "Explain in Telugu what is backpropagation in neural networks." Another area to explore is the model's performance on specific downstream tasks. The paper accompanying the model release provides some initial zero-shot evaluation results, but there may be opportunities to fine-tune or adapt the model for more specialized applications.
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bloomz-560m
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The bloomz-560m model is part of the BLOOMZ & mT0 family of models developed by the BigScience workshop. These models are capable of following human instructions in dozens of languages zero-shot by finetuning the BLOOM and mT5 pretrained multilingual language models on the BigScience team's crosslingual task mixture dataset (xP3). The resulting models demonstrate strong crosslingual generalization abilities to unseen tasks and languages. The bloomz-560m model in particular is a 560M parameter version of the BLOOMZ model, recommended for prompting in English. Similar models in the BLOOMZ & mT0 family include smaller and larger versions ranging from 300M to 176B parameters, as well as models finetuned on the xP3mt dataset for prompting in non-English languages. Model inputs and outputs Inputs Natural language prompts describing a desired task or output Instructions can be provided in any of the 46 languages the model was trained on Outputs Coherent text outputs continuing or completing the provided prompt Outputs can be in any of the model's supported languages Capabilities The bloomz-560m model can be used to perform a wide variety of natural language generation tasks, from translation to creative writing to question answering. For example, given the prompt "Translate to English: Je t'aime", the model is likely to respond with "I love you." Other potential prompts include suggesting related search terms, writing a story, or explaining a technical concept in another language. What can I use it for? The bloomz-560m model is well-suited for research, education, and open-ended language exploration. Researchers could use the model to study zero-shot learning and cross-lingual generalization, while educators could leverage it to create multilingual learning materials. Developers may find the model useful as a base for fine-tuning on specific downstream tasks. Things to try One interesting aspect of the BLOOMZ models is the importance of clear prompting. The performance can vary depending on how the input is phrased - it's important to make it clear when the input stops to avoid the model trying to continue the prompt. For example, the prompt "Translate to English: Je t'aime" without a full stop at the end may result in the model continuing the French sentence. Better prompts include adding a period, or explicitly stating "Translation:". Providing additional context, like specifying the desired output language, can also improve the model's performance.
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bloomz-3b
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The bloomz-3b model is part of the BLOOMZ & mT0 model family developed by the BigScience workshop. These models are capable of following human instructions in dozens of languages zero-shot by finetuning the BLOOM and mT5 pretrained multilingual language models on the BigScience crosslingual task mixture (xP3) dataset. The bloomz and bloomz-7b1 models are similar larger scale versions of the bloomz-3b model. Model inputs and outputs The bloomz-3b model is a text-to-text transformer language model. It takes natural language prompts as input and generates corresponding text outputs. Inputs Natural language prompts or instructions in dozens of languages Outputs Coherent text continuations and completions of the input prompts Responses to natural language instructions and tasks Capabilities The bloomz-3b model is capable of performing a wide variety of natural language tasks in a zero-shot manner, including translation, question answering, summarization, and open-ended text generation. For example, given the prompt "Translate to English: Je taime.", the model will likely respond with "I love you." The model can also be instructed to generate creative content, explain technical concepts, and solve problems expressed in natural language. What can I use it for? The bloomz-3b model is well-suited for research, education, and creative applications that involve natural language processing and generation. Developers could integrate the model into applications that require language understanding and generation capabilities, such as chatbots, virtual assistants, or content creation tools. Researchers may use the model to explore topics in machine learning, linguistics, and cognitive science. Educators could leverage the model to generate learning materials or engage students in language-based activities. Things to try One interesting aspect of the BLOOMZ models is their ability to follow instructions and prompts in multiple languages. Try providing the model with prompts in different languages, such as "Explain backpropagation in neural networks in Hindi." or "Write a fairy tale about a troll saving a princess from a dragon in Spanish." The model's crosslingual generalization capabilities allow it to understand and respond to instructions across a wide range of languages.
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bloomz-7b1-mt
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The bloomz-7b1-mt model is a multilingual language model developed by the BigScience research workshop. It is a variant of the BLOOM model that has been fine-tuned on a cross-lingual task mixture (xP3) dataset to improve its ability to follow human instructions and perform tasks in multiple languages. The model has 7.1 billion parameters and was trained using a variety of computational resources, including a Jean Zay Public Supercomputer. Model inputs and outputs Inputs Natural language prompts or instructions in a wide range of languages, including English, Mandarin Chinese, Spanish, Hindi, and many others. Outputs Coherent text continuations or responses in the same language as the input prompt, following the given instructions or completing the requested task. Capabilities The bloomz-7b1-mt model is capable of understanding and generating text in dozens of languages, allowing it to perform a variety of cross-lingual tasks. It can translate between languages, answer questions, summarize text, and even generate creative content like stories and poems. The model's multilingual capabilities make it a powerful tool for language learning, international communication, and multilingual applications. What can I use it for? The bloomz-7b1-mt model can be used for a wide range of natural language processing tasks, including: Machine translation between languages Question answering in multiple languages Text summarization across languages Creative writing assistance in different languages Language learning and practice Developers and researchers can fine-tune the model for more specific use cases, or use it as a starting point for building multilingual AI applications. Things to try Some interesting things to try with the bloomz-7b1-mt model include: Providing prompts in different languages and observing the model's ability to understand and respond appropriately. Experimenting with the model's code generation capabilities by giving it prompts to write code in various programming languages. Exploring the model's ability to maintain coherence and consistency when responding to multi-turn conversations or tasks that span multiple languages. Evaluating the model's performance on specialized tasks or domains, such as scientific or legal text, to assess its broader applicability. By testing the model's capabilities and limitations, users can gain valuable insights into the current state of multilingual language models and help drive future advancements in this important area of AI research.
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