qwen微调 - 技术栈

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# env
apt-get update
apt-get install git-lfs
git init
git lfs install
mkdir Qwen
cd Qwen
git clone https://huggingface.co/Qwen/Qwen-7B
cd ..
git clone https://gitee.com/meijunhui0917/LLaMA-Efficient-Tuning.git
git clone "huanhuan": {
    "file_name": "huanhuan.json",
    "columns": {
      "prompt": "instruction",
      "query": "input",
      "response": "output"
    }
  },

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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
    --stage sft \
    --model_name_or_path ../Qwen/Qwen-7B\
    --do_train \
    --dataset huanhuan \
    --template default \
    --finetuning_type lora \
    --lora_target c_attn \
    --output_dir ./model \
    --overwrite_cache \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 1000 \
    --learning_rate 5e-5 \
    --num_train_epochs 3.0 \
    --plot_loss \
    --fp16

python 复制代码

CUDA_VISIBLE_DEVICES=0 python  src/train_bash.py \
    --stage sft \
    --model_name_or_path ../Qwen/Qwen-7B \
    --do_train True \
    --overwrite_cache True \
    --finetuning_type lora \
    --template chatml \
    --dataset huanhuan \
    --max_source_length 512 \
    --max_target_length 512 \
    --learning_rate 5e-05 \
    --num_train_epochs 3.0 \
    --max_samples 100000 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --max_grad_norm 1.0 \
    --logging_steps 5 \
    --save_steps 100 \
    --warmup_steps 0 \
    --padding_side left \
    --lora_rank 8 \
    --lora_dropout 0.1 \
    --lora_target c_attn \
    --resume_lora_training True \
    --output_dir saves/Qwen-7B-chat/lora/2023-08-22-17-23-51 \
    --fp16 True \
    --plot_loss True

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