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minimind/trainer
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neil.ge 859c8ee2a3 perf: enable fused AdamW in full SFT training 
Use PyTorch's fused AdamW implementation to reduce optimizer kernel
launch overhead and speed up the training step on CUDA devices.
Guard with device check so the CPU-only fallback path is unaffected.

Co-authored-by: TRAE CLI <traecli@bytedance.com>
 		2026-09-05 18:37:04 +08:00
..
rollout_engine.py [fix] inference bug 2026-05-03 20:48:48 +08:00
train_agent.py [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
train_distillation.py [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
train_dpo.py [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
train_full_sft.py perf: enable fused AdamW in full SFT training 2026-09-05 18:37:04 +08:00
train_grpo.py [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
train_lora.py [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
train_ppo.py [refactor] PPO cleanup 2026-07-23 11:11:05 +08:00
train_pretrain.py [update] remove expert args 2026-09-04 22:16:38 +08:00
train_tokenizer.py [update] minimind-3 2026-03-25 23:57:45 +08:00
trainer_utils.py [update] minimind-3 2026-03-25 23:57:45 +08:00