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Introducing Poro 34B: A Breakthrough AI Model
Revolutionizing Language Models
State-of-the-art language models require vast amounts of text data for pretraining, posing a challenge for smaller languages. Multilingual training offers a practical solution to enhance models for smaller languages, mitigating data scarcity issues.
Practical Solutions and Value
Researchers have developed Poro 34B, a 34-billion-parameter model trained on 1 trillion tokens of Finnish, English, and programming languages. This approach significantly enhances the capabilities of existing Finnish models, excels in translation, and remains competitive in English and programming tasks.
Training Process
The dataset underwent preprocessing to eliminate low-quality and duplicate texts and filter out toxic contexts. Tokenization involved a custom byte-level BPE tokenizer with a 128K token vocabulary. The model was trained to 1 trillion tokens, surpassing the estimated optimal compute for efficiency.
Performance and Versatility
Poro 34B demonstrates strong performance across English, Finnish, and code tasks, showcasing low character-level perplexity and commendable coherence and grammatical correctness in open-ended generation tasks. Its impressive capabilities outperform dedicated translation models and even Google Translate.
Future Implications
The release of Poro 34B seeks to serve as a template for creating larger models for other smaller languages, facilitating further research and development.
Unlock the Power of AI with Poro 34B
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