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  This repository hosts both the raw/processed pre-training data and the instruction-following datasets used for supervised fine-tuning (SFT).
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  ## 📊 Dataset Summary
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  * **Total Token Count:** ~300 Billion tokens
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  ### 2. `fine-tune` (Instruction/SFT Corpus)
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  A curated subset of high-quality prompt-completion pairs formatted for dialogue and instruction following.
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- * **ChatML Format Ready:** The data structure natively aligns with standard ChatML prompt templates (`<|bos|>`, `<|eos|>`, role definitions).
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  * **Tasks:** Includes physical commonsense reasoning (PIQA), general reasoning (HellaSwag), multi-task accuracy (MMLU), and Russian language understanding (XWinograd RU).
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  ---
 
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  This repository hosts both the raw/processed pre-training data and the instruction-following datasets used for supervised fine-tuning (SFT).
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+ # tokenizer is located at LLM_D6 in my models!
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  ## 📊 Dataset Summary
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  * **Total Token Count:** ~300 Billion tokens
 
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  ### 2. `fine-tune` (Instruction/SFT Corpus)
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  A curated subset of high-quality prompt-completion pairs formatted for dialogue and instruction following.
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+ * **ChatML Format Ready:** The data structure natively aligns with standard ChatML prompt templates (`<|bos|>`, `<|end_of_text|>`, role definitions).
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  * **Tasks:** Includes physical commonsense reasoning (PIQA), general reasoning (HellaSwag), multi-task accuracy (MMLU), and Russian language understanding (XWinograd RU).
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  ---