Llama-3.1-Sherkala-8B-Chat
| Property | Value |
|---|---|
| Parameters | 8 Billion |
| Context Length | 8,192 tokens |
| Languages | Kazakh (primary), English, Russian, Turkish |
| License | cc-by-nc-sa-4.0 |
| Developer | Inception (G42), MBZUAI, Cerebras Systems |
What is Llama-3.1-Sherkala-8B-Chat?
Llama-3.1-Sherkala-8B-Chat is a state-of-the-art multilingual language model specifically designed to excel in Kazakh language processing while maintaining strong capabilities in English, Russian, and Turkish. Built on the Llama-3.1 architecture, it features a custom-extended vocabulary optimized for Kazakh morphology and has been trained on 45.3 billion tokens of diverse multilingual content.
Implementation Details
The model builds upon a decoder-only transformer architecture utilizing RoPE positional encoding and grouped-query attention. Its vocabulary has been expanded by 25% to better handle Kazakh language features, resulting in improved tokenization efficiency and better overall performance.
- Advanced multilingual training on 45.3B tokens
- Custom tokenizer with 25% vocabulary expansion
- Extensive safety alignment and cultural adaptation
- Trained on Cerebras CS-2 systems with pure data parallelism
Core Capabilities
- Superior performance in Kazakh language tasks (47.6 average score on benchmarks)
- Strong multilingual understanding across English and Russian
- Robust instruction following and chat capabilities
- Cultural alignment and safety features
- 8K token context window for handling longer conversations
Frequently Asked Questions
Q: What makes this model unique?
Sherkala stands out for its specialized optimization for the Kazakh language while maintaining strong multilingual capabilities. It's the first major language model to achieve state-of-the-art performance in Kazakh while being competitive in English and Russian tasks.
Q: What are the recommended use cases?
The model is ideal for research in Kazakh NLP, including chat assistants, question answering, content generation, and multilingual applications. It's particularly suited for academic research, businesses targeting Kazakh-speaking audiences, and developers building Kazakh language applications.
