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# 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