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Found 3 papers, 1 papers with code
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
This study led to Pangu Ultra MoE, a sparse LLM with 718 billion parameters, and we conducted experiments on the model to verify the simulation results.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions
With instruction tuning, Large Language Models (LLMs) can enhance their ability to adhere to commands.
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