jais-13b
Maintainer: core42
127
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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
jais-13b is a 13 billion parameter pre-trained bilingual large language model developed by Inception, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and Cerebras Systems. The model is trained on a dataset containing 72 billion Arabic tokens and 279 billion English/code tokens, with the Arabic data iterated over for 1.6 epochs and the English/code for 1 epoch, for a total of 395 billion tokens.
The jais-13b model is based on a transformer-based decoder-only (GPT-3) architecture and uses SwiGLU non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate to long sequence lengths and providing improved context handling and model precision.
Compared to similar large language models like XVERSE-13B and Baichuan-7B, jais-13b stands out for its bilingual Arabic-English capabilities and strong performance on the C-EVAL and MMLU benchmarks.
Model inputs and outputs
Inputs
- Text data: The
jais-13bmodel takes text input data, either in Arabic or English.
Outputs
- Generated text: The model outputs generated text, either in Arabic or English, based on the input prompt.
Capabilities
The jais-13b model has strong performance on standard benchmarks for both Arabic and English language understanding and generation. It achieves state-of-the-art results on the C-EVAL and MMLU benchmarks, outperforming other models of similar size.
Some example capabilities of the jais-13b model include:
- Generating coherent, contextually relevant text in both Arabic and English
- Answering questions and completing tasks that require understanding of the input text
- Translating between Arabic and English
- Summarizing long-form text in both languages
What can I use it for?
The jais-13b model can be used as a foundation for a wide range of NLP applications that require strong language understanding and generation capabilities in both Arabic and English. Some potential use cases include:
- Developing multilingual chatbots and virtual assistants
- Building machine translation systems between Arabic and English
- Automating content generation and summarization for Arabic and English text
- Powering search and information retrieval systems that handle both languages
To use the jais-13b model, you can follow the provided getting started guide, which includes sample code for loading the model and generating text.
Things to try
One interesting aspect of the jais-13b model is its ability to handle long input sequences thanks to the use of ALiBi position embeddings. You could experiment with providing the model with longer prompts or context and see how it performs on tasks that require understanding and reasoning over a larger amount of information.
Another area to explore could be fine-tuning the model on specific domains or tasks, such as Arabic-English machine translation or question-answering, to further enhance its capabilities in those areas. The Jais and Jais-chat paper discusses these potential fine-tuning approaches.
Overall, the jais-13b model represents a significant advancement in large language models that can handle both Arabic and English, and provides a powerful foundation for a wide range of multilingual NLP 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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The jais-13b is a 13 billion parameter pre-trained bilingual large language model for both Arabic and English, developed by Inception, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and Cerebras Systems. It was trained on a dataset containing 72 billion Arabic tokens and 279 billion English/code tokens. The model is based on a transformer-based decoder-only (GPT-3) architecture and uses SwiGLU non-linearity, as well as ALiBi position embeddings to enable the model to handle long sequence lengths and provide improved context handling. The jais-13b model achieves state-of-the-art performance on a comprehensive Arabic test suite, outperforming other leading models like BLOOM, LLaMA2, AraT5, and AraBART across a range of tasks including question answering, common sense reasoning, and language understanding. In comparison, the similar jais-13b-chat model has been fine-tuned for chatbot and instruction-following capabilities. Model inputs and outputs Inputs Text data**: The jais-13b model accepts text data as input, supporting both Arabic and English. Outputs Generated text**: The model generates text output in response to the input. This can include answers to questions, continuations of prompts, or any other form of open-ended text generation. Capabilities The jais-13b model demonstrates strong performance on a variety of Arabic and English language tasks, including question answering, common sense reasoning, and language understanding. For example, it achieved an average score of 46.5% on the comprehensive EXAMS benchmark, outperforming other large language models like BLOOM (40.9%), LLaMA2 (38.1%), AraT5 (32.0%), and AraBART (36.7%). The model's ability to handle long sequence lengths and provide improved context handling also makes it well-suited for tasks like multi-turn dialogue, knowledge-intensive question answering, and text summarization. What can I use it for? The jais-13b model can be used for a wide range of applications targeting Arabic and English speakers, such as: Research**: Researchers can use the model as a base for further fine-tuning and development of Arabic and bilingual language models. Commercial use**: The model can be used as a starting point for building chatbots, virtual assistants, and other customer service applications targeting Arabic-speaking audiences. The similar jais-13b-chat model is specifically designed for this purpose. The model's open-source license and support for free commercial use make it an attractive option for developers and businesses looking to incorporate advanced Arabic and bilingual language capabilities into their products and services. Things to try One interesting aspect of the jais-13b model is its ability to handle long sequence lengths and provide improved context handling, thanks to the use of ALiBi position embeddings. This could be leveraged for tasks like multi-turn dialogue, where the model needs to maintain context and coherence over an extended conversation. Researchers and developers could also explore fine-tuning the jais-13b model on specialized datasets or tasks, such as domain-specific question answering or summarization, to further enhance its capabilities for targeted applications.
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