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  # HuYaLM 100B
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- **Hu**gging Face **YaLM 100B** (by [BlackSamorez](https://github.com/BlackSamorez)) is a _transformers_ compatible implementation of **YaLM 100B** model, originally trained by Yandex for 65 days on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, books, and countless other sources in both English and Russian.
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- This particular implementation was motivated by the fact that the model was originally published with outdated code, incompatible with latest achievemnt in the field. This code, being compatible with _transformers_, should automatically support much needed features such as [quantization](https://huggingface.co/docs/transformers/main_classes/quantization) and [adapter training](https://huggingface.co/docs/peft/index).
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- Training details and best practices on acceleration and stabilizations can be found on **[Medium](https://medium.com/p/d1df53d0e9a6)** (English) and **[Habr](https://habr.com/ru/company/yandex/blog/672396/)** (Russian) articles. The original code published by Yandex can be found on [GitHub](https://github.com/yandex/YaLM-100B).
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- This code, as well as the model itself, is published under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license, permitting commercial use.
 
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  # HuYaLM 100B
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+ **Hu**gging Face **YaLM 100B** (by [BlackSamorez](https://github.com/BlackSamorez)) is a **transformers-compatible** implementation of the **YaLM 100B** model. Originally trained by Yandex, the model used 800 A100 graphics cards and 1.7 TB of diverse text data, including online texts and books, in both English and Russian.
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+ The motivation behind this particular implementation is to update the originally published, outdated code to align with the latest advancements in the field. As this code is compatible with the _transformers_ library, it inherently supports crucial features like [quantization](https://huggingface.co/docs/transformers/main_classes/quantization) (for model size optimization) and [adapter training](https://huggingface.co/docs/peft/index) (for efficient fine-tuning).
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+ For more details on training, acceleration, and stabilization techniques, you can refer to articles on **[Medium](https://medium.com/p/d1df53d0e9a6)** (in English) and **[Habr](https://habr.com/ru/company/yandex/blog/672396/)** (in Russian). The original code from Yandex is available on [GitHub](https://github.com/yandex/YaLM-100B).
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+ This code and model are distributed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license, which allows for commercial use.