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README.md
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@@ -30,7 +30,7 @@ The fine-tuning process utilized the [Continued Pretraining](https://docs.unslot
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- **Languages Supported**: English and Russian
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- **Training Method**: [Continued Pretraining](https://docs.unsloth.ai/basics/continued-pretraining)
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- **Epochs**: 10
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- **Resources**: 1 RTX A5000 24Gb vRAM for approximately
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- **License:** This model is distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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### Quantization Options
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- **Поддерживаемые языки**: Русский и английский
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- **Метод обучения**: [Continued Pretraining](https://docs.unsloth.ai/basics/continued-pretraining)
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- **Количество эпох**: 10
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- **Оборужование**: 1 RTX A5000 24Gb vRAM приблизительно в течение
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- **Лицензия:** Модель распространяетчя по лицензии Google [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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### Варианты квантизации
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- **Languages Supported**: English and Russian
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- **Training Method**: [Continued Pretraining](https://docs.unsloth.ai/basics/continued-pretraining)
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- **Epochs**: 10
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- **Resources**: 1 RTX A5000 24Gb vRAM for approximately 36 hours. The cost of finetuning is about $8.
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- **License:** This model is distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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### Quantization Options
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| 197 |
- **Поддерживаемые языки**: Русский и английский
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| 198 |
- **Метод обучения**: [Continued Pretraining](https://docs.unsloth.ai/basics/continued-pretraining)
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- **Количество эпох**: 10
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| 200 |
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- **Оборужование**: 1 RTX A5000 24Gb vRAM приблизительно в течение 36 часов. Общие затраты на файнтюнинг около $8.
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- **Лицензия:** Модель распространяетчя по лицензии Google [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
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### Варианты квантизации
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| 203 |
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