chore: import upstream snapshot with attribution

examples/train/think_model/deepseek_r1.sh

@@ -0,0 +1,29 @@
+# 18GB
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model deepseek-ai/DeepSeek-R1-0528-Qwen3-8B \
+    --tuner_type lora \
+    --dataset 'swift/DeepSeek-R1-Qwen3-8B-Distill#1800' \
+              'swift/self-cognition:empty_think#600' \
+    --loss_scale ignore_empty_think \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps 16 \
+    --load_from_cache_file false \
+    --eval_steps 50 \
+    --save_steps 50 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --use_liger_kernel true \
+    --model_author swift \
+    --model_name swift-robot

examples/train/think_model/qwen3_demo1.sh

@@ -0,0 +1,31 @@
+# use `--loss_scale ignore_empty_think`
+# Avoid losing the think capability by ignoring the loss of empty `<think>\n\n</think>\n\n`
+# This method is also applicable to the Deepseek-R1 series of models.
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen3-8B \
+    --tuner_type lora \
+    --dataset 'swift/Qwen3-SFT-Mixin#2000' \
+              'swift/self-cognition:empty_think#600' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 50 \
+    --save_steps 50 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --use_liger_kernel true \
+    --load_from_cache_file false \
+    --loss_scale ignore_empty_think \
+    --model_author swift \
+    --model_name swift-robot

examples/train/think_model/qwen3_demo2.sh

@@ -0,0 +1,30 @@
+# use `swift/self-cognition:qwen3`
+# Avoid losing the thinking capability by appending `/no_think` to the dataset query.
+# https://github.com/modelscope/ms-swift/blob/77985c2ccdac8ed4037174ee222e79d1f1d5059d/swift/llm/dataset/dataset/llm.py#L835
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen3-8B \
+    --tuner_type lora \
+    --dataset 'swift/Qwen3-SFT-Mixin#2000' \
+              'swift/self-cognition:qwen3#600' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 50 \
+    --save_steps 50 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --use_liger_kernel true \
+    --load_from_cache_file false \
+    --model_author swift \
+    --model_name swift-robot