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Key Technical Specifications
Model Architecture
Built on a 24B dense transformer architecture, Mistral Saba is specifically optimized for Arabic and South Asian languages while maintaining strong general capabilities. The model features advanced multilingual training techniques to ensure high performance across its target languages.
Performance Metrics
The model demonstrates exceptional performance across multilingual benchmarks:
- MBZUAI Arabic MMLU (0-shot): 77.39
- Arabic MT-Bench-dev (internal translation & gpt-4o-2024-08-06 judge): 7.93
- English MT-Bench dev (gpt-4o-2024-05-13 judge): 7.98
Use Cases
Translation & Language Support
Translates between Arabic, Farsi, Urdu, Hebrew, and Indic languages while preserving cultural context and nuance. Valuable for international businesses, educational institutions, and government agencies requiring accurate cross-cultural communication.
Content Creation & Adaptation
Creates and adapts content across multiple languages while maintaining message integrity. Helps organizations develop culturally relevant materials for Arabic and South Asian markets, benefiting content creators, marketers, and educators.
Best Practices
- Language Context: Provide prompts in the target language for optimal performance and cultural relevance
- Context Window: Leverage the 32K token capacity for comprehensive documents and extended conversations
- Few-shot prompting: Include examples in your prompts when working with complex linguistic or cultural tasks
Get Started with Mistral Saba 24B
Experience the exceptional multilingual capabilities of mistral-saba-24b with Groq speed:
shell
pip install groqPython
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
model="mistral-saba-24b",
messages=[
{
"role": "user",
"content": "Explain why fast inference is critical for reasoning models"
}
]
)
print(completion.choices[0].message.content)