Damo-nlp-mt

polylm-13b

DAMO-NLP-MT

51

PolyLM is a multilingual large language model developed by DAMO-NLP-MT. It is trained on 640 billion tokens across 18 languages, including Chinese, English, Spanish, German, French, and more. This model improves upon existing multilingual models like LLaMA and BLOOM by integrating bilingual data into the training and using a curriculum learning strategy to increase the proportion of non-English data over time. PolyLM is available in two sizes: 1.7 billion and 13 billion parameters. Model inputs and outputs PolyLM is a decoder-only language model that can be used for a variety of text-to-text tasks. It takes in natural language prompts or instructions and generates relevant text outputs. Inputs Natural language prompts or instructions in any of the 18 supported languages Outputs Generated text outputs in the same language as the input prompt Outputs can be used for tasks like language generation, translation, question answering, and more Capabilities PolyLM demonstrates strong multilingual capabilities, outperforming other open-source models like LLaMA and BLOOM on various multilingual tasks while maintaining comparable performance in English. It can be used for tasks like multilingual understanding, question answering, generation, and translation. What can I use it for? PolyLM can be used as a powerful multilingual language model for a variety of natural language processing applications. Some potential use cases include: Multilingual content generation: Automatically generating high-quality text in multiple languages for websites, marketing materials, product descriptions, and more. Machine translation: Fine-tuning the model for machine translation between any of the 18 supported languages. Multilingual question answering: Building chatbots or virtual assistants that can understand and respond to queries in multiple languages. Multilingual text summarization: Summarizing long-form content in various languages. Things to try One interesting thing to try with PolyLM is its multilingual self-instruction capabilities. The model was trained using a method that automatically generates over 132,000 diverse multilingual instructions, allowing it to better understand and follow instructions across languages. You could experiment with providing the model with prompts or instructions in different languages and see how it responds. Another idea is to fine-tune PolyLM on a specific multilingual task or domain to further improve its performance. The flexibility of the model allows it to be adapted for a wide range of applications beyond just open-ended language generation.

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