Post
2790
Geilim-1B-SR-Instruct โ Serbian Intelligence for Deep Reasoning ๐ง ๐ท๐ธ
NoesisLab/Geilim-1B-SR-Instruct
Geilim-1B-SR-Instruct is a lightweight Large Language Model (LLM) designed to bring advanced reasoning capabilities to low-resource languages. It focuses on Serbian understanding and generation while maintaining robust English reasoning. Built on the LLaMA-3 architecture with a proprietary hybrid reasoning mechanism, it delivers deep logic while keeping outputs concise and natural. ๐
Core Innovations ๐ก
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. ๐ธ๏ธ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. โก
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. ๐
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. ๐ป
Use Cases ๐ ๏ธ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. ๐ฃ๏ธ
Educational Tools: Multi-turn interactive tasks and learning support. ๐
Key Advantages โจ
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. โ
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. ๐
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. ๐ค
NoesisLab/Geilim-1B-SR-Instruct
Geilim-1B-SR-Instruct is a lightweight Large Language Model (LLM) designed to bring advanced reasoning capabilities to low-resource languages. It focuses on Serbian understanding and generation while maintaining robust English reasoning. Built on the LLaMA-3 architecture with a proprietary hybrid reasoning mechanism, it delivers deep logic while keeping outputs concise and natural. ๐
Core Innovations ๐ก
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. ๐ธ๏ธ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. โก
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. ๐
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. ๐ป
Use Cases ๐ ๏ธ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. ๐ฃ๏ธ
Educational Tools: Multi-turn interactive tasks and learning support. ๐
Key Advantages โจ
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. โ
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. ๐
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. ๐ค
