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Can LLMs be implemented in non-English languages?

Iguazio supports any and all languages for gen AI applications. Today, there are projects in English, Turkish, Arabic, Portuguese, Spanish, Hebrew, Norwegian and more. New languages are implemented either through translations or by fine-tuning models in the new language.

Fine-tuning and implementing LLMs in non-English languages involves several key steps and considerations:

1. Fine-Tuning: Fine-tune the selected LLM on the collected data in the target language. This involves training the model on language-specific tasks or objectives, such as language modeling, text classification, machine translation, etc.

2. Evaluation: Evaluate the fine-tuned model on appropriate benchmarks or validation datasets to assess its performance, accuracy, and generalization ability in the target language.

3. Language-specific challenges: Address any language-specific challenges or nuances during the development process. These may include morphological complexity, syntactic differences, lack of labeled data, and domain-specific terminology.

4. Adaptation: Adapt the model to specific applications or use cases in the target language. This may involve domain adaptation, transfer learning, or customization of model outputs to meet the requirements of the application.

5. Testing and iteration: Test the developed LLM application rigorously in real-world scenarios to identify and address any issues or limitations. Iterate on the development process as needed to improve performance and user experience.

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