Text Generation
Transformers
Safetensors
Finnish
English
bloom
Inference Endpoints
text-generation-inference
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@@ -16,7 +16,7 @@ _**NOTE:** This is a **research checkpoint** of a model for which **training has
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  Poro is a 34B parameter decoder-only transformer pretrained on Finnish, English and code. It is being trained on 1 trillion tokens (300 billion as of this release). Poro is a fully open source model and is made available under the Apache 2.0 License.
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- Poro was created in a collaboration between SiloGen from [Silo AI](https://www.silo.ai/), the [TurkuNLP group](https://turkunlp.org/) of the University of Turku, and [High Performance Language Technologies](https://hplt-project.org/) (HPLT). Training was conducted on the LUMI supercomputer, using compute resources generously provided by CSC - IT Center for Science, Finland.
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  This project is part of an ongoing effort to create open source large language models for non-English and especially low resource languages like Finnish. Through the combination of English and Finnish training data we get a model that outperforms previous Finnish only models, while also being fluent in English and code, and capable of basic translation between English and Finnish.
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  Poro is a 34B parameter decoder-only transformer pretrained on Finnish, English and code. It is being trained on 1 trillion tokens (300 billion as of this release). Poro is a fully open source model and is made available under the Apache 2.0 License.
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+ Poro was created in a collaboration between [SiloGen](https://www.silo.ai/silogen) from [Silo AI](https://www.silo.ai/), the [TurkuNLP group](https://turkunlp.org/) of the University of Turku, and [High Performance Language Technologies](https://hplt-project.org/) (HPLT). Training was conducted on the [LUMI supercomputer](https://www.lumi-supercomputer.eu/), using compute resources generously provided by [CSC](https://csc.fi/) - IT Center for Science, Finland.
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  This project is part of an ongoing effort to create open source large language models for non-English and especially low resource languages like Finnish. Through the combination of English and Finnish training data we get a model that outperforms previous Finnish only models, while also being fluent in English and code, and capable of basic translation between English and Finnish.
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