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video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/lora_llm_full_vit/merge_lora.sh

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+CUDA_VISIBLE_DEVICES=0 \
+swift export \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --merge_lora true
+
+# CUDA_VISIBLE_DEVICES=0 \
+# swift infer \
+#     --model output/vx-xxx/checkpoint-xxx-merged \
+#     --stream true \
+#     --load_data_args true \
+#     --temperature 0 \
+#     --max_new_tokens 2048

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/lora_llm_full_vit/sft.sh

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+# 4 * 22GiB
+# vit/merger lr 1e-5; llm lora lr 1e-4
+NPROC_PER_NODE=4 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'AI-ModelScope/coco#20000' \
+    --train_type custom \
+    --external_plugins 'examples/train/multimodal/lora_llm_full_vit/custom_plugin.py' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --vit_lr 1e-5 \
+    --aligner_lr 1e-5 \
+    --lora_rank 16 \
+    --lora_alpha 32 \
+    --gradient_accumulation_steps 4 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --deepspeed zero2 \
+    --save_only_model true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/ocr.sh

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+# 20GB
+CUDA_VISIBLE_DEVICES=0 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --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

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/omni/infer.sh

@@ -0,0 +1,10 @@
+CUDA_VISIBLE_DEVICES=0 \
+VIDEO_MAX_PIXELS=50176 \
+FPS_MAX_FRAMES=12 \
+MAX_PIXELS=1003520 \
+ENABLE_AUDIO_OUTPUT=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --stream true \
+    --load_data_args true \
+    --max_new_tokens 2048

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/omni/sft.sh

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+# 4*35GB
+# A demo for four modalities that can be run directly
+pip install transformers -U
+
+nproc_per_node=4
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+ENABLE_AUDIO_OUTPUT=1 \
+NPROC_PER_NODE=$nproc_per_node \
+VIDEO_MAX_PIXELS=50176 \
+FPS_MAX_FRAMES=12 \
+MAX_PIXELS=1003520 \
+ENABLE_AUDIO_OUTPUT=0 \
+swift sft \
+    --model Qwen/Qwen2.5-Omni-7B \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#2000' \
+              'AI-ModelScope/LaTeX_OCR:human_handwrite#2000' \
+              'speech_asr/speech_asr_aishell1_trainsets:validation#2000' \
+              'swift/VideoChatGPT:all#2000' \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --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 \
+    --deepspeed zero2

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/rlhf/dpo/full.sh

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+# 4 * 50GiB
+nproc_per_node=4
+
+PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=$nproc_per_node \
+MAX_PIXELS=1003520 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'swift/RLAIF-V-Dataset#20000' \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --freeze_vit true \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --deepspeed zero3 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --save_only_model true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/rlhf/dpo/lora.sh

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+# 4 * 50GiB
+# You can refer to `https://github.com/QwenLM/Qwen2.5-VL` for the meaning of the `MAX_PIXELS` parameter.
+# --rlhf_type cpo/orpo/simpo/rm are also supported
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+MAX_PIXELS=1003520 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'swift/RLAIF-V-Dataset#20000' \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --deepspeed zero2 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/rlhf/kto.sh

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+# Due to the absence of a multi-modal open-source dataset for kto,
+# we will use a pure text kto dataset as an example here.
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+MAX_PIXELS=1003520 \
+swift rlhf \
+    --rlhf_type kto \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'AI-ModelScope/ultrafeedback-binarized-preferences-cleaned-kto#10000' \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --deepspeed zero2 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/video.sh

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+# 4*80GB
+# You can refer to `https://github.com/QwenLM/Qwen2.5-VL` for the meaning of the `VIDEO_MAX_PIXELS` parameter.
+nproc_per_node=4
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=$nproc_per_node \
+VIDEO_MAX_PIXELS=50176 \
+FPS_MAX_FRAMES=12 \
+swift sft \
+    --model Qwen/QVQ-72B-Preview \
+    --dataset swift/VideoChatGPT:all \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --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 \
+    --deepspeed zero3

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/multimodal/vit_gradient_checkpointing.sh

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+# gc true, vgc true: 48GiB, 2.45s/it
+# gc true, vgc false: 62GiB 2.32s/it
+# gc false, vgc true: 56GiB 2.16s/it
+# gc false, vgc false: 77GiB 1.95s/it
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=4 \
+VIDEO_MAX_PIXELS=50176 \
+FPS_MAX_FRAMES=12 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset swift/VideoChatGPT:all \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --learning_rate 1e-5 \
+    --freeze_vit false \
+    --freeze_aligner false \
+    --gradient_accumulation_steps 1 \
+    --gradient_checkpointing true \
+    --vit_gradient_checkpointing true \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero3 \
+    --use_liger_kernel true \
+    --attn_impl flash_attn \
+    --padding_free true \
+    --save_only_model true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/optimizer/muon.sh

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+# 17GB
+# ref: https://github.com/MoonshotAI/Moonlight/blob/master/examples/toy_train.py
+# `moonshotai/Moonlight-16B-A3B-Instruct` does not support training; here we use `Qwen/Qwen2.5-7B-Instruct` as an example.
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
+              'AI-ModelScope/alpaca-gpt4-data-en#500' \
+              'swift/self-cognition#500' \
+    --optimizer muon \
+    --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 \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --model_author swift \
+    --model_name swift-robot

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/packing/llm.sh

@@ -0,0 +1,31 @@
+# 22GB
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --packing true \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
+              'AI-ModelScope/alpaca-gpt4-data-en#500' \
+              'swift/self-cognition#500' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 3 \
+    --attn_impl flash_attn \
+    --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 4 \
+    --eval_steps 50 \
+    --save_steps 50 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --model_author swift \
+    --model_name swift-robot

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/packing/qwen2_5_omni.sh

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+# 4 * 32GB
+# Multimodal packing currently only supports qwen2_vl, qwen2_5_vl, qwen2_5_omni, internvl2_5/3
+# A demo for four modalities that can be run directly
+# For local datasets, it is recommended to use streaming: `--streaming true` (save memory)
+pip install transformers -U
+
+NPROC_PER_NODE=4 \
+ENABLE_AUDIO_OUTPUT=1 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+VIDEO_MAX_PIXELS=50176 \
+FPS_MAX_FRAMES=12 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2.5-Omni-7B \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#10000' \
+              'AI-ModelScope/LaTeX_OCR#2000' \
+              'speech_asr/speech_asr_aishell1_trainsets:validation#2000' \
+    --train_type lora \
+    --torch_dtype bfloat16 \
+    --attn_impl flash_attn \
+    --packing true \
+    --num_train_epochs 3 \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 50 \
+    --save_steps 50 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 8 \
+    --deepspeed zero2

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/packing/qwen2_5_vl.sh

@@ -0,0 +1,35 @@
+# 4 * 36GB
+# Multimodal packing currently only supports qwen2_vl, qwen2_5_vl, qwen2_5_omni, internvl2_5/3
+# Efficiency: With packing: 10 minutes; Without packing: >=1 hour
+# For local datasets, it is recommended to use streaming: `--streaming true` (save memory)
+# You can also use padding_free to avoid the space/time cost caused by multi-modal packing:
+# https://github.com/modelscope/ms-swift/blob/main/examples/train/padding_free/sft.sh
+
+NPROC_PER_NODE=4 \
+MAX_PIXELS=1003520 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --train_type lora \
+    --dataset 'AI-ModelScope/LaTeX_OCR#20000' \
+    --torch_dtype bfloat16 \
+    --attn_impl flash_attn \
+    --packing true \
+    --num_train_epochs 3 \
+    --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 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 8 \
+    --deepspeed zero2

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/packing/streaming.sh

@@ -0,0 +1,34 @@
+# 4 * 36GB
+# A demo using the Hugging Face dataset
+# The first model weights will be saved around step 70.
+NPROC_PER_NODE=4 \
+MAX_PIXELS=1003520 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+HF_ENDPOINT=https://hf-mirror.com \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --train_type lora \
+    --dataset 'HF::linxy/LaTeX_OCR:full#20000' \
+    --torch_dtype bfloat16 \
+    --attn_impl flash_attn \
+    --streaming true \
+    --shuffle_buffer_size 1000 \
+    --packing true \
+    --save_strategy epoch \
+    --max_steps 1000 \
+    --max_epochs 5 \
+    --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 1 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 1 \
+    --dataset_num_proc 8 \
+    --deepspeed zero2

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/padding_free/dpo.sh

@@ -0,0 +1,29 @@
+# with padding_free: 4 * 47GiB, 1.90s/it
+# without padding_free: 4 * 57GiB 3.32s/it
+NPROC_PER_NODE=4 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type full \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --save_only_model true \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --deepspeed zero3 \
+    --attn_impl flash_attn \
+    --save_only_model true \
+    --padding_free true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/padding_free/dpo_vlm.sh

@@ -0,0 +1,31 @@
+# with padding_free: 4 * 53GiB, 3.55s/it
+# without padding_free: 4 * 62GiB 4.41s/it
+PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=4 \
+MAX_PIXELS=1003520 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'swift/RLAIF-V-Dataset#20000' \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 4 \
+    --per_device_eval_batch_size 4 \
+    --learning_rate 1e-5 \
+    --freeze_vit true \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --deepspeed zero3 \
+    --logging_steps 5 \
+    --max_length 4096 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --attn_impl flash_attn \
+    --save_only_model true \
+    --padding_free true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/padding_free/sft.sh

@@ -0,0 +1,29 @@
+# Supported multimodal models reference:
+# https://github.com/modelscope/ms-swift/blob/main/examples/train/packing/qwen2_5_vl.sh
+# without padding_free: 4 * 60GiB, 26h
+# padding_free: 4 * 44GiB, 13h
+NPROC_PER_NODE=4 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift sft \
+    --model Qwen/Qwen2.5-7B \
+    --train_type full \
+    --dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \
+    --torch_dtype bfloat16 \
+    --per_device_train_batch_size 8 \
+    --per_device_eval_batch_size 8 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 200 \
+    --save_steps 200 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 8 \
+    --dataset_num_proc 8 \
+    --save_total_limit 2 \
+    --save_only_model true \
+    --output_dir output/Qwen2.5-7B \
+    --deepspeed zero3 \
+    --use_liger_kernel true \
+    --attn_impl flash_attn \
+    --padding_free true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/plugins/channel_loss.sh

@@ -0,0 +1,32 @@
+# use loss_type channel_loss
+# channels specifies the channels included in the dataset
+# data should have 'channel' field
+# eg.
+# {"channel": "chat",
+#   "messages": [
+#     {"role": "system", "content": "You are a helpful assistant"},
+#     {"role": "user", "content": "What color do you like?"},
+#     {"role": "assistant", "content": "I like blue."}
+#   ]}
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-0.5B-Instruct \
+    --dataset '/path/to/channel_dataset' \
+    --train_type full \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 8 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 1 \
+    --max_length 512 \
+    --output_dir output \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --loss_type channel_loss \
+    --channels 'chat' 'math' 'code'

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/plugins/loss_scale.sh

@@ -0,0 +1,22 @@
+# loss_scale all to train all tokens
+# use loss_type loss_scale
+# This is just an example
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset 'swift/self-cognition#1000' \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --model_author swift \
+    --model_name swift-robot \
+    --loss_scale all \
+    --loss_type loss_scale

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/plugins/tuner_phi4_mm.sh

@@ -0,0 +1,20 @@
+# `--train_type dummy`
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model LLM-Research/Phi-4-multimodal-instruct \
+    --dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \
+    --train_type dummy \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-4 \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 200 \
+    --save_steps 200 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/predict_with_generate/train.sh

@@ -0,0 +1,28 @@
+# 20GiB
+CUDA_VISIBLE_DEVICES=0 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-7B-Instruct \
+    --dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \
+    --train_type lora \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 2 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --freeze_vit true \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --predict_with_generate true \
+    --metric_for_best_model rouge-l \
+    --greater_is_better true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/pretrain/train.sh

@@ -0,0 +1,31 @@
+# If not using flash_attn, or transformers<4.44,
+# or encountering an abnormally large loss (i.e., the model does not support packing),
+# please remove `--packing true`.
+nproc_per_node=4
+
+NPROC_PER_NODE=$nproc_per_node \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift pt \
+    --model Qwen/Qwen2.5-7B \
+    --train_type full \
+    --dataset swift/chinese-c4 \
+    --torch_dtype bfloat16 \
+    --streaming true \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
+    --packing true \
+    --eval_steps 500 \
+    --save_steps 500 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --deepspeed zero3 \
+    --max_length 8192 \
+    --max_steps 10000 \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 8 \
+    --save_only_model true \
+    --output_dir output/Qwen2.5-7B \
+    --attn_impl flash_attn

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/qlora/awq.sh

@@ -0,0 +1,28 @@
+# 10GB
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct-AWQ \
+    --train_type lora \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
+              'AI-ModelScope/alpaca-gpt4-data-en#500' \
+              'swift/self-cognition#500' \
+    --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 \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --model_author swift \
+    --model_name swift-robot

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/qlora/bnb.sh

@@ -0,0 +1,34 @@
+# 10GB
+# pip install bitsandbytes
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
+              'AI-ModelScope/alpaca-gpt4-data-en#500' \
+              'swift/self-cognition#500' \
+    --torch_dtype bfloat16 \
+    --bnb_4bit_compute_dtype bfloat16 \
+    --bnb_4bit_quant_type nf4 \
+    --bnb_4bit_use_double_quant true \
+    --quant_method bnb \
+    --quant_bits 4 \
+    --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 \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --model_author swift \
+    --model_name swift-robot

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/qlora/gptq.sh

@@ -0,0 +1,25 @@
+# 2 * 30GiB
+CUDA_VISIBLE_DEVICES=0,1 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2.5-VL-72B-Instruct-GPTQ-Int4 \
+    --dataset 'modelscope/coco_2014_caption:validation#20000' \
+    --train_type lora \
+    --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 \
+    --freeze_vit true \
+    --gradient_accumulation_steps 16 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/qlora/hqq.sh

@@ -0,0 +1,31 @@
+# 10GB
+# pip install hqq
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
+              'AI-ModelScope/alpaca-gpt4-data-en#500' \
+              'swift/self-cognition#500' \
+    --torch_dtype bfloat16 \
+    --quant_method hqq \
+    --quant_bits 4 \
+    --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 \
+    --system 'You are a helpful assistant.' \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --model_author swift \
+    --model_name swift-robot

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rft/rft.py

@@ -0,0 +1,224 @@
+import os
+import shutil
+import subprocess
+import time
+from typing import List
+
+from swift.utils import get_device_count
+
+# NOTE: this script supports at most 8 GPUS in a node, if using multi node, please use custom logic.
+
+# Paste conda env
+# conda_prefix = 'source /root/miniconda3/etc/profile.d/conda.sh && conda activate py311 && '
+conda_prefix = ''
+
+
+def do_sample(model: str, model_type: str, dataset: List[str], iter: int):
+    device_count = get_device_count()
+    handlers = []
+    datasets = []
+    # Sampling cache, to avoid lmdeploy & PRM run at the same time
+    # Why lmdeploy not vllm? we found that the responses generated by lmdeploy are more similar than ones of vllm.
+    for device in range(device_count):
+        sample_cmd = (f'{conda_prefix} USE_OPENCOMPASS_EVALUATOR=True CUDA_VISIBLE_DEVICES={device} swift sample '
+                      f'--model {model} --model_type {model_type} '
+                      f'--dataset {" ".join(dataset)} '
+                      f'--data_range {device} {device_count} '
+                      f'--max_length 2048 '
+                      f'--system "You are a math model, you should **think step by step** carefully, '
+                      f'and always consider the basic math principles to avoid making calculating mistakes.'
+                      f'Give the final answer wrapped with \\boxed{{}}" '
+                      f'--load_args false '
+                      f'--sampler_engine vllm '
+                      f'--max_new_tokens 768 '
+                      f'--override_exist_file true '
+                      f'--num_sampling_per_gpu_batch_size 1 '
+                      f'--num_return_sequences 64 '
+                      f'--cache_files sample_output/iter_{iter}_proc_{device}_cache.jsonl '
+                      f'--output_file iter_{iter}_proc_{device}_cache.jsonl '
+                      f'--top_p 1.0 '
+                      f'--temperature 1.0 ')
+        print(f'Sampling caches of iter {iter}, part {device}.', flush=True)
+        env = os.environ.copy()
+        env['CUDA_VISIBLE_DEVICES'] = str(device)
+        handler = subprocess.Popen(
+            f'{sample_cmd}' + f' > logs/sample_iter_{iter}_proc_{device}_cache.log 2>&1',
+            env=os.environ.copy(),
+            shell=True,
+            executable='/bin/bash')
+        handlers.append(handler)
+
+    for proc, handler in enumerate(handlers):
+        handler.wait()
+        assert os.path.exists(os.path.join('sample_output', f'iter_{iter}_proc_{proc}_cache.jsonl'))
+
+    handlers = []
+    # Sample again, this time to filter with ORM & PRM
+    # Provide your PRM model or PRM name(add PRM in plugin/prm.py first)
+    # You can define your custom PRM logic in the plugin
+    # (like, split your steps, use the worst score/last score/avg score)
+    for device in range(device_count):
+        sample_cmd = (
+            f'{conda_prefix} USE_OPENCOMPASS_EVALUATOR=True CUDA_VISIBLE_DEVICES={device} swift sample '
+            f'--model {model} --model_type {model_type} '  # change to --resume_from_checkpoint to use the latest optimizer state # noqa
+            f'--dataset {" ".join(dataset)} '
+            f'--data_range {device} {device_count} '
+            f'--max_length 2048 '
+            f'--system "You are a math model, you should **think step by step** carefully, '
+            f'and always consider the basic math principles to avoid making calculating mistakes.'
+            f'Give the final answer wrapped with \\boxed{{}}" '
+            f'--load_args false '
+            f'--sampler_engine no '
+            f'--orm_model math '  # math defines in plugin/orm.py
+            f'--prm_model Qwen/Qwen2.5-Math-PRM-7B '
+            f'--prm_threshold {min(0.7 + 0.1*iter, 0.9)} '
+            f'--max_new_tokens 768 '
+            f'--override_exist_file true '  # no not override the existing sample files
+            f'--num_sampling_per_gpu_batch_size 1 '
+            f'--num_return_sequences 64 '
+            f'--output_file iter_{iter}_proc_{device}_sampling.jsonl '
+            f'--cache_files sample_output/iter_{iter}_proc_{device}_cache.jsonl ')
+        print(f'Sampling iter {iter}, part {device}.', flush=True)
+        env = os.environ.copy()
+        env['CUDA_VISIBLE_DEVICES'] = str(device)
+        handler = subprocess.Popen(
+            f'{sample_cmd}' + f' > logs/sample_iter_{iter}_proc_{device}.log 2>&1',
+            env=os.environ.copy(),
+            shell=True,
+            executable='/bin/bash')
+        handlers.append(handler)
+
+    for proc, handler in enumerate(handlers):
+        handler.wait()
+        assert os.path.exists(os.path.join('sample_output', f'iter_{iter}_proc_{proc}_sampling.jsonl')), (
+            f'{os.path.join("sample_output", f"iter_{iter}_proc_{proc}_sampling.jsonl")} not exists, '
+            'please check the sample logs to get the detail error.')
+        datasets.append(os.path.join('sample_output', f'iter_{iter}_proc_{proc}_sampling.jsonl'))
+    print(f'Sampling done, files:{datasets}', flush=True)
+    return datasets
+
+
+def do_train(model: str, model_type: str, datasets: List[str], iter, cmd='sft'):
+    gpu_prefix = ''
+    ds_config = ''
+    if get_device_count() > 1:
+        gpu_prefix = f'NPROC_PER_NODE={get_device_count()} '
+        ds_config = '--deepspeed zero3 '
+    extra_args = ''
+    if cmd == 'rlhf':
+        extra_args = '--rlhf_type dpo --beta 0.3 '  # use another reinforce learning method supported by swift
+    ga = 128 // get_device_count() // 2
+    train_cmd = (f'{conda_prefix} {gpu_prefix} swift {cmd} '
+                 f'--model {model} --model_type {model_type} '
+                 f'--dataset {" ".join(datasets)} '
+                 f'--max_length 2048 '
+                 f'--num_train_epochs 1 '
+                 f'--load_args false '
+                 f'--train_type full '
+                 f'{extra_args} '
+                 f'--eval_strategy no '
+                 f'--split_dataset_ratio 0 '
+                 f'--per_device_train_batch_size 2 '
+                 f'--gradient_accumulation_steps {ga} '
+                 f'--save_steps 1 '
+                 f'--save_strategy epoch '
+                 f'{ds_config} '
+                 f'--learning_rate 4e-6 ')
+
+    print(f'Training iter {iter}.', flush=True)
+    handler = subprocess.Popen(
+        f'{train_cmd}' + f' > logs/train_iter_{iter}.log 2>&1',
+        shell=True,
+        env=os.environ.copy(),
+        executable='/bin/bash')
+    handler.wait()
+    ckpt = None
+    with open(f'logs/train_iter_{iter}.log', 'r') as f:
+        for line in f.readlines():
+            if 'last_model_checkpoint: ' in line:
+                ckpt = line.split('last_model_checkpoint: ')[1]
+                break
+    assert ckpt is not None
+    print(f'Training done, ckpt: {ckpt.strip()}.', flush=True)
+    return ckpt.strip()
+
+
+def do_eval(model, model_type: str, iter):
+    eval_cmd = (
+        f'{conda_prefix} swift eval '
+        '--eval_dataset competition_math '  # eval another dataset
+        '--infer_backend vllm --eval_limit 500 '
+        f'--model {model} --model_type {model_type} '
+        '--system "You are a math model, you should **think step by step** carefully, '
+        'and always consider the basic math principles to avoid making calculating mistakes. '
+        'Give the final answer wrapped with \\boxed{}"')
+    print('Evaluating.', flush=True)
+    # Replace the original dataset to the math.json, this is for test, comment this if not need
+    replace_math_dataset()
+
+    if iter is None:
+        iter = 'origin'
+    env = os.environ.copy()
+    env['CUDA_VISIBLE_DEVICES'] = '0'
+    handler = subprocess.Popen(
+        f'{eval_cmd}' + f' > logs/eval_iter_{iter}.log 2>&1', shell=True, env=env, executable='/bin/bash')
+    handler.wait()
+
+    acc = None
+    # | math | 393424 | accuracy | gen | 39.00 |
+    with open(f'logs/eval_iter_{iter}.log', 'r') as f:
+        for line in f.readlines():
+            if 'Level 5' in line and 'AveragePass@1' in line:
+                parts = [p for p in line.split('|') if p.strip()]
+                acc = float(parts[-2])
+                break
+
+    print(f'Iter {iter} eval done with acc: {acc}.', flush=True)
+    return acc
+
+
+def replace_math_dataset():
+    # Note: This may run failed because this is special for math test,
+    # and one must run swift eval --eval_dataset math first to make sure opencompass has created
+    # the folder.
+    # You can use original math dataset either. just comment this call.
+    user_dir = os.path.expanduser('~')
+    if os.path.exists(os.path.join(user_dir, '.cache', 'opencompass', 'data', 'math', 'math.json')):
+        os.remove(os.path.join(user_dir, '.cache', 'opencompass', 'data', 'math', 'math.json'))
+    shutil.copy(
+        os.path.join('examples', 'train', 'rft', 'math.json'),
+        os.path.join(user_dir, '.cache', 'opencompass', 'data', 'math', 'math.json'))
+
+
+def main():
+    os.makedirs('logs', exist_ok=True)
+    max_acc = 0.
+    first_model = 'Qwen/Qwen2.5-Math-7B-Instruct'
+    model_type = 'qwen2_5_math'
+
+    if False:
+        # eval the original model
+        do_eval(first_model, None)
+
+    model = first_model
+    for i in range(5):
+        ts = time.time()
+        datasets = do_sample(model, model_type, ['tastelikefeet/competition_math'], i)
+        # add custom data filter here, for example: length or diversity control
+        print(f'do sample cost: {(time.time()-ts) / 60:.1f} minutes.', flush=True)
+        ts = time.time()
+        # if want to train the original dataset with datasets, add the original dataset here
+        # if want to train the original model everytime, change to first_model
+        ckpt = do_train(model, model_type, datasets, i)
+        print(f'do train cost: {(time.time() - ts) / 60:.1f} minutes.', flush=True)
+        ts = time.time()
+        acc = do_eval(ckpt, model_type, i)
+        print(f'do eval cost: {(time.time() - ts) / 60:.1f} minutes.', flush=True)
+        if acc > max_acc:
+            max_acc = acc
+        model = ckpt
+        print(f'acc: {acc}, upgrade model to : {model}', flush=True)
+
+
+if __name__ == '__main__':
+    main()

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/README.md

@@ -0,0 +1,3 @@
+# TIPS
+
+Multi-modal models' RLHF are also supported! Check the multimodal folder for details.

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/cpo.sh

@@ -0,0 +1,28 @@
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type cpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --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 $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/dpo/full.sh

@@ -0,0 +1,26 @@
+# 4 * 50GiB
+NPROC_PER_NODE=4 \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type full \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps 4 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 8192 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --save_only_model true \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --deepspeed zero3 \
+    --attn_impl flash_attn

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/dpo/lora.sh

@@ -0,0 +1,27 @@
+# 24GiB
+# It is recommended to use padding_free. For more details, please refer to:
+# https://github.com/modelscope/ms-swift/blob/main/examples/train/padding_free/dpo.sh
+CUDA_VISIBLE_DEVICES=0 \
+swift rlhf \
+    --rlhf_type dpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --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 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/kto.sh

@@ -0,0 +1,27 @@
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type kto \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset 'AI-ModelScope/ultrafeedback-binarized-preferences-cleaned-kto#10000' \
+    --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 $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/orpo.sh

@@ -0,0 +1,28 @@
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type orpo \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --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 $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/ppo/full.sh

@@ -0,0 +1,33 @@
+# 8 * 65 GiB
+# Currently, it only supports the case where the model and reward_model use the same template/tokenizer.
+# Currently, multimodal model PPO is not supported.
+nproc_per_node=8
+
+PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type ppo \
+    --model LLM-Research/Meta-Llama-3.1-8B-Instruct \
+    --reward_model 'AI-ModelScope/Skywork-Reward-Llama-3.1-8B-v0.2' \
+    --train_type full \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#20000' 'AI-ModelScope/alpaca-gpt4-data-en#20000' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-6 \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero3 \
+    --response_length 512 \
+    --temperature 0.7 \
+    --dataset_num_proc 4 \
+    --save_only_model true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/ppo/lora.sh

@@ -0,0 +1,36 @@
+# 4 * 50GiB
+# Currently, it only supports the case where the model and reward_model use the same template/tokenizer.
+# Currently, multimodal model PPO is not supported.
+nproc_per_node=4
+
+PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type ppo \
+    --model LLM-Research/Meta-Llama-3.1-8B-Instruct \
+    --reward_model 'AI-ModelScope/Skywork-Reward-Llama-3.1-8B-v0.2' \
+    --train_type lora \
+    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#20000' 'AI-ModelScope/alpaca-gpt4-data-en#20000' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --response_length 512 \
+    --temperature 0.7 \
+    --dataset_num_proc 4 \
+    --save_only_model true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/rm.sh

@@ -0,0 +1,28 @@
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type rm \
+    --model Qwen/Qwen2.5-7B-Instruct \
+    --train_type lora \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --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 $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/rlhf/simpo.sh

@@ -0,0 +1,26 @@
+# 2*50GB
+nproc_per_node=2
+
+CUDA_VISIBLE_DEVICES=0,1 \
+NPROC_PER_NODE=$nproc_per_node \
+swift rlhf \
+    --rlhf_type simpo \
+    --model Qwen/Qwen2.5-3B-Instruct \
+    --train_type full \
+    --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --learning_rate 1e-5 \
+    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --deepspeed zero2 \
+    --dataset_num_proc 4

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/bert/deploy.sh

@@ -0,0 +1,11 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift deploy \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --served_model_name bert-base-chinese \
+    --truncation_strategy right \
+    --max_length 512
+
+# curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+# "model": "bert-base-chinese",
+# "messages": [{"role": "user", "content": "包装差，容易被调包。"}]
+# }'

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/bert/infer.sh

@@ -0,0 +1,7 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true \
+    --max_batch_size 16 \
+    --truncation_strategy right \
+    --max_length 512

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/bert/sft.sh

@@ -0,0 +1,28 @@
+# If `num_labels` is provided, it will be considered a classification task,
+# and AutoModelForSequenceClassification will be used to load the model.
+# The BERT model does not require templates, so it can usually be used without registration.
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model AI-ModelScope/bert-base-chinese \
+    --train_type lora \
+    --dataset 'DAMO_NLP/jd:cls#2000' \
+    --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 512 \
+    --truncation_strategy right \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --num_labels 2 \
+    --task_type seq_cls

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/multi_label/sft.sh

@@ -0,0 +1,28 @@
+# Custom dataset format reference: https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-0.5B \
+    --train_type lora \
+    --dataset '<your-dataset>' \
+    --torch_dtype bfloat16 \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 16 \
+    --per_device_eval_batch_size 16 \
+    --learning_rate 1e-4 \
+    --lora_rank 8 \
+    --lora_alpha 32 \
+    --target_modules all-linear \
+    --gradient_accumulation_steps 1 \
+    --eval_steps 100 \
+    --save_steps 100 \
+    --save_total_limit 2 \
+    --logging_steps 5 \
+    --max_length 2048 \
+    --output_dir output \
+    --warmup_ratio 0.05 \
+    --dataloader_num_workers 4 \
+    --dataset_num_proc 4 \
+    --num_labels '<num-labels>' \
+    --task_type seq_cls \
+    --use_chat_template false \
+    --problem_type multi_label_classification

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/qwen2_5/deploy.sh

@@ -0,0 +1,8 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift deploy \
+    --adapters output/vx-xxx/checkpoint-xxx
+
+# curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+# "model": "Qwen2.5-0.5B",
+# "messages": [{"role": "user", "content": "包装差，容易被调包。"}]
+# }'

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/qwen2_5/infer.sh

@@ -0,0 +1,5 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true \
+    --max_batch_size 16

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/qwen2_5/sft.sh

@@ -0,0 +1,28 @@
+# If `num_labels` is provided, it will be considered a classification task,
+# and AutoModelForSequenceClassification will be used to load the model.
+# You can also specify `--model Qwen/Qwen2.5-0.5B-Instruct --use_chat_template true`.
+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+    --model Qwen/Qwen2.5-0.5B \
+    --train_type lora \
+    --dataset 'DAMO_NLP/jd:cls#2000' \
+    --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 \
+    --num_labels 2 \
+    --task_type seq_cls \
+    --use_chat_template false

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/qwen2_vl/infer.sh

@@ -0,0 +1,5 @@
+CUDA_VISIBLE_DEVICES=0 \
+MAX_PIXELS=1003520 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/qwen2_vl/sft.sh

@@ -0,0 +1,28 @@
+# If `num_labels` is provided, it will be considered a classification task.
+# You can also specify `--model Qwen/Qwen2.5-VL-2B-Instruct --use_chat_template true`.
+CUDA_VISIBLE_DEVICES=0 \
+MAX_PIXELS=1003520 \
+swift sft \
+    --model Qwen/Qwen2-VL-2B \
+    --train_type lora \
+    --dataset 'tany0699/garbage265#20000' \
+    --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 \
+    --num_labels 265 \
+    --task_type seq_cls \
+    --use_chat_template false

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/regression/deploy.sh

@@ -0,0 +1,8 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift deploy \
+    --adapters output/vx-xxx/checkpoint-xxx
+
+# curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
+# "model": "Qwen2.5-0.5B",
+# "messages": [{"role": "user", "content": "Task: Based on the given two sentences, provide a similarity score between 0.0 and 1.0.\nSentence 1: The animal is eating.\nSentence 2: A woman is dancing.\nSimilarity score: "}]
+# }'

video_gen_14d/third_party/VBench/VBench-2.0/vbench2/third_party/Instance_detector/examples/train/seq_cls/regression/infer.sh

@@ -0,0 +1,5 @@
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/vx-xxx/checkpoint-xxx \
+    --load_data_args true \
+    --max_batch_size 16