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Training Llama 3.3 Swallow: A Japanese sovereign LLM on Amazon SageMaker HyperPod
- The Institute of Science Tokyo successfully trained Llama 3.3 Swallow, a 70-billion-parameter LLM with enhanced Japanese capabilities, using Amazon SageMaker HyperPod.
- Llama 3.3 Swallow outperformed GPT-4o-mini and other models in Japanese language tasks, detailed in a report by Kazuki Fujii.
- The model is available in different variants on Hugging Face for research and development purposes.
- The training methodology involved continual pre-training and supervised fine-tuning for Japanese dialogue and code tasks.
- The base model displayed superior performance compared to industry models like GPT-4o-mini and Qwen2.5-72B.
- Licensing allows public usage of the model for research and commercial applications.
- The training infrastructure architecture utilized Amazon SageMaker HyperPod for high performance and scalability.
- Key elements included comprehensive storage hierarchy, compute and network configuration, and a robust observability stack.
- The project emphasized advanced parallelism strategies and optimized distributed training with Megatron-LM.
- Implemented memory prediction tools and checkpointing strategies further enhanced training efficiency.
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