In the vast world of language models, Greek has found its voice with the introduction of Meltemi, the first large foundation language model tailored specifically for the Greek language. Developed by the Institute for Language and Speech Processing at the Athena Research Innovation Center, Meltemi expands on the capabilities of the renowned Mistral-7B model by integrating a finely-tuned proficiency in Greek.
Why Meltemi?
Meltemi is designed to address the linguistic needs of Greek speakers everywhere. By building upon a colossal body of high-quality Greek text data, this model extends its linguistic reach and offers improved performance in various applications.
Key Features of Meltemi
- Built on the foundation of Mistral-7B with a Greek vocabulary extension
- Supports a context length of 8192 tokens
- Utilizes a large training corpus of approximately 40 billion tokens, ensuring nuanced understanding of the Greek language
- Includes both monolingual Greek and English data, bolstering bilingual capabilities
Understanding the Architecture with an Analogy
Imagine Meltemi as a skilled translator attending a grand feast (language data). The Mistral-7B model initially sets the table with a variety of dishes (knowledge), but Meltemi adds Greek favorites, enhancing the overall experience for Greek guests (users). By ensuring there are not just Greek dishes but also a mix of international cuisines (English texts), Meltemi creates a well-rounded dining experience, allowing for various cultural exchanges and deeper conversations (conversations and interactions in Greek). It’s a mix of both worlds, crafted to serve the linguistic palate of its Greek audience.
How to Use Meltemi
When utilizing Meltemi for your projects, remember to include the Beginning of Sequence (BOS) token in your tokenized prompts. Some frameworks may not include this by default, so be sure to check this setting!
Evaluation Results
Meltemi isn’t just theoretical; evaluation results show its effectiveness across several test sets. Below is a comparison that highlights the performance improvements:
| Model | Medical MCQA EL (15-shot) | Belebele EL (5-shot) | HellaSwag EL (10-shot) | ARC-Challenge EL (25-shot) | TruthfulQA MC2 EL (0-shot) | MMLU EL (5-shot) | Average |
|-------------------------------|---------------------------|----------------------|------------------------|----------------------------|-----------------------------|------------------|----------|
| Mistral 7B | 29.8% | 45.0% | 36.5% | 27.1% | 45.8% | 35% | 36.5% |
| Meltemi 7B | 41.0% | 63.6% | 61.6% | 43.2% | 52.1% | 47% | 51.4% |
Troubleshooting Common Issues
If you run into any issues while working with Meltemi, consider the following troubleshooting tips:
- Ensure that the correct token (BOS) is included in your inputs.
- Verify that your training dataset is adequately prepared and filtered.
- Check compatibility with your evaluation framework, especially with lm-eval-harness.
- If you encounter misleading or harmful outputs, remember that the model has not been aligned with human preferences.
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Ethical Considerations
While Meltemi showcases remarkable linguistic capabilities, it is important to acknowledge that it has not been fine-tuned to align with human ethical standards. Consequently, it may generate misleading or harmful content. Users are encouraged to apply critical thinking when utilizing the model’s outputs.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.