PartAI/Dorna-Llama3-8B-Instruct
PartAI/Dorna-Llama3-8B-Instruct is an 8 billion parameter instruction-tuned decoder-only language model developed by Part AI, built upon Meta Llama 3 Instruct. It is specifically trained and fine-tuned on Persian data, making it highly optimized for tasks requiring understanding and generation in the Persian language. This model excels across various tasks including Boolean Questions, Code Generation, Long Response, Math, News QA, Paraphrasing, General Knowledge, and Summarization, particularly demonstrating strong performance against other Persian-specific models.
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Dorna-Llama3-8B-Instruct Overview
Dorna-Llama3-8B-Instruct is an 8 billion parameter instruction-tuned model developed by Part AI, specifically fine-tuned on Persian data. It is built upon the robust Meta Llama 3 Instruct architecture, enhancing its capabilities for Persian language tasks.
Key Capabilities & Performance
This model has undergone extensive evaluation, both human and automatic (using GPT-4 as a judge), across a diverse set of tasks including:
- Boolean Questions (Complex and Easy)
- Code Generation
- Long Response (General and Historical)
- Math (Complex and Easy)
- News QA (Complex and Easy)
- Paraphrasing
- General Knowledge (Easy and Hard)
- Summarization
Notably, in human evaluations, Dorna-8B-it demonstrates strong performance, winning against "Persian Mind 7B" in 55.77% of cases and showing competitive results against "Meta-Llama-3-8B-Instruct" and "GPT 3.5 turbo-1106".
Automatic evaluations further highlight its strengths, with Dorna-8B-it achieving high win rates against several other models, including "Llama 3 base" (58.96% overall win rate), "Part Mistral 7B" (77.20%), "Persian Mind 7B" (90.88%), and "PersianLlama 7B" (98.70%).
Ideal Use Cases
This model is particularly well-suited for applications requiring high-quality Persian language understanding and generation, especially in conversational AI, content creation, and information retrieval within a Persian context. Its fine-tuning on Persian data makes it a strong candidate for tasks where other general-purpose models might underperform in this specific language.