RefalMachine
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README.md
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---
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datasets:
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- IlyaGusev/saiga_scored
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- IlyaGusev/saiga_preferences
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- dichspace/darulm
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language:
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- ru
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pipeline_tag: text-generation
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base_model:
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- RefalMachine/ruadapt_qwen2.5_3B_ext_u48_full_lr5e4_peft_mlp_32_32_bs256
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---
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# Model description
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Instruction-tuned version of RefalMachine/ruadapt_qwen2.5_3B_ext_u48_full_lr5e4_peft_mlp_32_32_bs256 with extended tokenizer after LEP (Learned Embedding Propagation, paper will be soon) procedure.
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Thanks to the extended tokenizer, the model works more efficiently with the Russian language (up to 60% speed up compared to Qwen-2.5-3B-Instruct in terms of characters)
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# How to cite:
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Tikhomirov M., Chernyshev D. Facilitating large language model Russian adaptation with Learned Embedding Propagation // 2024 (will be soon)
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Tikhomirov M., Chernyshev D. Impact of Tokenization on LLaMa Russian Adaptation //2023 Ivannikov Ispras Open Conference (ISPRAS). – IEEE, 2023. – С. 163-168.
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