Post
2774
Geilim-1B-SR-Instruct β Serbian Intelligence for Deep Reasoning π§ π·πΈ
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. π
Core Innovations π‘
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. πΈοΈ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. β‘
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. π
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. π»
Use Cases π οΈ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. π£οΈ
Educational Tools: Multi-turn interactive tasks and learning support. π
Key Advantages β¨
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. β
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. π
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. π€
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. π
Core Innovations π‘
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. πΈοΈ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. β‘
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. π
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. π»
Use Cases π οΈ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. π£οΈ
Educational Tools: Multi-turn interactive tasks and learning support. π
Key Advantages β¨
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. β
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. π
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. π€
