This article presents some of the best libraries available for LLM development, categorized by their specific roles in the project lifecycle.
GPT-2 has 1.5 billion parameters, and its parameters consume ~3GB of memory in 16-bit precision. However, one can hardly train it on a single GPU with 30GB of memory. That’s 10x the model’s memory, and you might wonder how that could be even possible. While the focus of this article is not LLM memory consumption , the example was used to help you reflect on the unfathomable scale and memory requirements of LLMs.
Additionally, DeepSpeed significantly transforms the training pipeline, which can introduce bugs and require significant testing. Additionally, DeepSpeed significantly transforms the training pipeline, which can introduce bugs and require significant testing. YaFSDP is a new data parallelism library that is an enhanced version of FSDP with additional optimizations, especially for LLMs.
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