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The paper details ufakzeka-1, a 151M-parameter Turkish language model built from scratch with a total cost of about $286, describing the training pipeline, evaluation methods, and key findings on small-model training limitations.
This paper compares chunking and embedding strategies for RAG systems in Turkish, evaluating their impact on document question answering through a systematic study with multiple configurations.
This paper presents embeddingmagibu-200m, a Turkish-focused sentence embedding model built via cross-lingual tokenizer surgery and offline distillation, achieving strong performance on Turkish benchmarks with a cost-quality trade-off.