The paper reports on adapting the Qwen3 language model to Tajik using small LoRA adapters across sizes from 0.6B to 8B. While the adapters consistently lowered Tajik text loss by 0.60 to 1.45 nats per token, they did not improve Tajik reading comprehension on the Belebele benchmark; in some cases accuracy even dropped. A more extensive 8B training pipeline, including 375 million tokens of raw Tajik text and supervised fine‑tuning, produced a new model (e3) that outperformed Qwen3‑8B on Belebele by 11.4 points, though it slightly degraded English MMLU performance.
By Tohir Saidzoda, Madina Abdusalomzoda, Nasimjon Rajabov, Akhrorkhon Ikromov, Komron Giyosiev, Abubakr Abdullozoda, Amin Jalilov, Jonibek Kishvarov, Rahmonali Sharipov, Mariyamkhon Shodyeva
Saidzoda‑Tajik‑LFM‑2.6B is a 2.6‑billion‑parameter hybrid model adapted for Tajik tool calling. The adaptation uses two stages: LoRA continued pretraining on 1.49 million cleaned Tajik web documents followed by one epoch of supervised fine‑tuning on machine‑translated Tajik tool calls, English tool calls, and Tajik instructions. On a 174‑item Tajik tool‑calling test set, strict full‑call accuracy rises from 20.69 % for the base model to 64.14 %, while English performance remains unchanged; however, continued pretraining removes the native tool‑call format and part of the skill, and the model shows limited general assistant capabilities.
By Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov, Abubakr Abdullozoda, Amin Jalilov, Jonibek Kishvarov, Rahmonali Sharipov, Mariyamkhon Shodyeva
arXiv:2610.18420v1 Announce Type: cross
Abstract: We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, m...
By Ramchand Kumaresan, Madhusudhanan G, Kaushik Kezendirane
Yalda-Embedding is a 595.8‑million‑parameter Persian retrieval model built by adapting Qwen3‑Embedding‑0.6B and trained on over one million query‑document pairs. It achieves the highest macro‑average score (0.5488) among 0.6B models on eight FaMTEB Persian retrieval tasks, outperforming Qwen3‑Embedding‑0.6B and F2LLM‑v2‑0.6B on every task and approaching the performance of the larger Qwen3‑Embedding‑4B. The authors also conduct an ablation study on contrastive batch size, optimization stabilization, and relational knowledge distillation, showing that the distilled configuration improves all tasks without increasing inference‑time model size and includes hard‑negative evaluation gains despite no explicit hard‑negative mining.
By Yalda Taheri, Mohammad Hassan Heydari, Armon Rasooli
The paper evaluates the tablet‑2 long‑term memory engine on multilingual text benchmarks and cross‑lingual photo retrieval without lexical matching. Tablet‑2 achieves high accuracy on LongMemEval‑S (95.7%) and moderate accuracy on BEAM‑1M (67.5%), with minimal variance across runs. In multimodal tests, it outperforms BM25 on image‑cell recall and shows significant language‑dependent performance gaps, especially for low‑resource languages.
By Sunwoo Kim
MoganColBERT-TR is a Turkish multi‑vector retrieval model that projects token embeddings from 768 to 128 dimensions and uses MaxSim late interaction for scoring. It builds on the previously trained MoganBERT‑TR encoder, adapting it to the ColBERT objective with a single‑epoch distillation phase using cross‑encoder teacher scores. Evaluated on five Turkish BEIR datasets in a zero‑shot setting, it achieves an overall score of 37.36, outperforming larger models on most datasets.
By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay