chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
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+# 2 * 60GiB
+# This is just a demo for DiffusionGemma training.
+# Notes:
+# 1. Currently only --per_device_train_batch_size 1 is supported,
+# and the response length of a single sample must be less than config.canvas_length.
+# 2. --gradient_checkpointing false must be set. DiffusionGemma's encoder passes
+# KV to the decoder via DynamicCache, and gradient checkpointing causes errors
+# when recomputing the forward pass during backward.
+# 3. For customizing the specific training loss, refer to:
+# https://github.com/modelscope/ms-swift/blob/104048e374b954b4df6961f83f77392031f38fb0/swift/template/templates/gemma.py#L386-L428
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=2 \
+swift sft \
+    --model google/diffusiongemma-26B-A4B-it \
+    --dataset 'sapientinc/sudoku-extreme-1k' \
+    --load_from_cache_file true \
+    --split_dataset_ratio 0.01 \
+    --tuner_type lora \
+    --torch_dtype bfloat16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --num_train_epochs 3 \
+    --loss_scale ignore_empty_think \
+    --gradient_checkpointing false \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --freeze_vit true \
+    --freeze_aligner true \
+    --gradient_accumulation_steps 4 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataset_num_proc 4 \
+    --deepspeed zero2 \
+    --dataloader_num_workers 4
+
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true \
+    --enable_thinking false