4 papers
Saidzoda-CA-EmbeddingGemma-300M: One Small Embedder for Tajik, Kazakh, Uzbek and Kyrgyz
Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov +5
The small open embedder EmbeddingGemma-300M, used as released, finds the right passage first for only 51.7% to 60.8% of Belebele questions in Tajik, Kazakh, Uzbek and Kyrgyz. We pr…
Saidzoda-Tajik-EmbeddingGemma-300M: Knowing Every Letter Is Not Reading Tajik
Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov +5
EmbeddingGemma-300M is a small open embedder whose tokenizer represents every Tajik letter, and as released it is weak at Tajik retrieval: on our Tajik law test the right passage i…
Saidzoda-Tajik-E5-Large: Fine-Tuning a 560M Embedder for Tajik Retrieval
Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov +5
Finding the passage that answers a question is the first step of most practical systems built on documents, and for Tajik we found no openly described model for it. We present Said…
Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling, and What Continued Pretraining Takes Away
Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov +5
We release Saidzoda-Tajik-LFM-2.6B, a Tajik adaptation of the 2.6B-parameter hybrid model LFM2.5-2.6B-Base, built for tool calling in Tajik on modest hardware. The recipe has two s…