7 citations · 20 across the 10 of their papers we have counts for
3 papers · 1 filter
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
Kazuki Fujii, Yukito Tajima, Sakae Mizuki +14
The performance of large language models (LLMs) in program synthesis and mathematical reasoning is fundamentally limited by the quality of their pre-training corpora. We introduce…
Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs
Kazuki Fujii, Taishi Nakamura, Rio Yokota
Large Language Models (LLMs) have attracted significant attention due to their human-like language understanding and generation capabilities, as well as their applicability across…
Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator
Kazuki Fujii, Kohei Watanabe, Rio Yokota
In large language model (LLM) training, several parallelization strategies, including Tensor Parallelism (TP), Pipeline Parallelism (PP), Data Parallelism (DP), as well as Sequence…