GUIDE · CODING · #2383
Train Qwen3 on AWS Trainium using torchtitan
A torchtitan walkthrough demonstrates three ways to train Qwen3 on a single AWS Trainium trn2.48xlarge: (1) via Transformers v5 backend (AutoModelForCausalLM) reading/writing Hugging Face safetensors; (2) using torchtitan’s native Qwen3Model via config.model_spec; and (3) adding NKI (Neuron Kernel Interface) mixture-of-experts kernels from torchtitan-neuron and attaching custom kernels with the override registry. The post covers environment setup (Neuron PyTorch container or AWS DL AMI), installing torchtitan-neuron, torchrun launch patterns, checkpoint loading/saving (safetensors), and reports a verified step-1 loss example for Qwen3-4B-Instruct-2507 (6.54 from published weights vs 12.43 from random init).
KEY POINTS
- A torchtitan walkthrough demonstrates three ways to train Qwen3 on a single AWS Trainium trn2.48xlarge: (1) via Transformers v5 backend (AutoModelForCausalLM) reading/writing Hugging Face safetensors; (2) using torchtitan’s native Qwen3Model via config.model_spec; and (3) adding NKI (Neuron Kernel Interface) mixture-of-experts kernels from torchtitan-neuron and attaching custom kernels with the override registry.
- The post covers environment setup (Neuron PyTorch container or AWS DL AMI), installing torchtitan-neuron, torchrun launch patterns, checkpoint loading/saving (safetensors), and reports a verified step-1 loss example for Qwen3-4B-Instruct-2507 (6.54 from published weights vs 12.43 from random init).
- This shows a practical, reproducible path to run and fine-tune Qwen3 on AWS Trainium (including native model support, Transformers v5 integration, and custom Neuron kernels), relevant for developers optimizing large-model training on Trainium hardware.
WHY IT MATTERS
This shows a practical, reproducible path to run and fine-tune Qwen3 on AWS Trainium (including native model support, Transformers v5 integration, and custom Neuron kernels), relevant for developers optimizing large-model training on Trainium hardware.