We present RIFA, a family of efficient small open-weight language models designed for practical bilingual text generation. The series consists of five models ranging from 0.5B to 3B parameters: Rifa- Nano (0.5B), RIFA-CODE (0.6B), RIFA-Edge (0.6B), RIFA-FLASH (1.7B), and RIFA-PRO (3B). Each model is fine-tuned for conversational use in both English and Bangla, with specialized roles covering ultra-lightweight deployment, code assistance, edge devices, balanced performance, and higher-quality generation. All models are released under the Apache 2.0 license with full weights, multiple GGUF quantizations, and clear documentation. RIFA prioritizes low resource requirements while remaining useful for real-world text generation tasks on constrained hardware. We describe the model lineup, design principles, and public release to support further research and practical adoption of efficient small language models.
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
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