
# YaLM 100B

https://github.com/yandex/YaLM-100B

**YaLM 100B** is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.

The model leverages 100 billion parameters. It took 65 days to train the model 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.

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.

{"language": ["en", "ru"], "license": "apache-2.0", "tags": ["gpt", "NLG"]}
tensorboard gpt NLG en ru license:apache-2.0 region:us
TAGS
#tensorboard #gpt #NLG #en #ru #license-apache-2.0 #region-us 


# YaLM 100B

URL

YaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.

The model leverages 100 billion parameters. It took 65 days to train the model 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.

Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles.

# YaLM 100B

URL

YaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.

The model leverages 100 billion parameters. It took 65 days to train the model 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.

Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles.
TAGS
#tensorboard #gpt #NLG #en #ru #license-apache-2.0 #region-us 
 # YaLM 100B

URL

YaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.

The model leverages 100 billion parameters. It took 65 days to train the model 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.

Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles.
passage: TAGS
#tensorboard #gpt #NLG #en #ru #license-apache-2.0 #region-us 
# YaLM 100B

URL

YaLM 100B is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.

The model leverages 100 billion parameters. It took 65 days to train the model 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.

Training details and best practices on acceleration and stabilizations can be found on Medium (English) and Habr (Russian) articles.