This dataset is created by Ivan Bondarenko for calibrating (importance matrix computation) and evaluating GGUF quantizations of large language models targeting Russian language, including but not limited to Meno-Lite-0.1-GGUF.

Purpose

Train split: calibration for llama.cpp quantization (any Russian-focused LLM).
Test split: quality evaluation (perplexity, etc.) via llama-perplexity or similar tools.

Dataset Composition

Train: Russian texts of fiction and official/business styles, including selected Wikipedia articles and sampled textual content from the Novosibirsk State University (NSU) website.
Test: randomly selected texts about various Russian universities.

Limitations

Domain bias: the dataset overrepresents academic, official, and Wikipedia-style Russian. It may not reflect informal, dialectal, or highly domain-specific language (e.g., medicine, law, technical manuals).
No multi-turn or dialogue: suitable only for causal LM evaluation, not conversational or instruction-following benchmarks.
Temporal cutoff: based on static snapshots (no streaming updates), so it may not reflect very recent language usage or events.

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