【AIGC】Llama2-7B-Chat模型微调

环境

微调框架：LLaMA-Efficient-Tuning
训练机器：4*RTX3090TI (24G显存)
python环境：python3.8, 安装requirements.txt依赖包

一、Lora微调

1、准备数据集

2、训练及测试

1）创建模型输出目录

mkdir -p models/llama2_7b_chat/llama-main/train_models/llama2_7b_chat_muti_gpus_01_epoch10/train_model

2）创建deepspeed配置文件目录

mkdir -p models/baichuan2_13b_chat/deepspeed_config

3）创建deepspeed配置文件

vi models/llama2_7b_chat/llama-main/deepspeed_config/llama2_7b_chat_muti_gpus_01_epoch10.json

{
  "bf16": {
    "enabled": true
  },
  "fp16": {
    "enabled": "auto",
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "initial_scale_power": 16,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "optimizer": {
    "type": "AdamW",
    "params": {
      "lr": "auto",
      "betas": "auto",
      "eps": "auto",
      "weight_decay": "auto"
    }
  },
  "scheduler": {
    "type": "WarmupDecayLR",
    "params": {
      "last_batch_iteration": -1,
      "total_num_steps": "auto",
      "warmup_min_lr": "auto",
      "warmup_max_lr": "auto",
      "warmup_num_steps": "auto"
    }
  },
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
      "device": "cpu",
      "pin_memory": true
    },
    "offload_param": {
      "device": "cpu",
      "pin_memory": true
    },
    "overlap_comm": true,
    "contiguous_gradients": true,
    "sub_group_size": 1e9,
    "reduce_bucket_size": "auto",
    "stage3_prefetch_bucket_size": "auto",
    "stage3_param_persistence_threshold": "auto",
    "stage3_max_live_parameters": 2e9,
    "stage3_max_reuse_distance": 2e9,
    "stage3_gather_16bit_weights_on_model_save": true
  },
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "steps_per_print": 2000,
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "wall_clock_breakdown": false
}

4）训练模型

deepspeed --num_gpus 2 --master_port=9902 src/train_bash1.py \
    --stage sft \
    --model_name_or_path models/llama2_7b_chat/origin_model/Llama-2-7b-chat-hf \
    --do_train \
    --dataset example1 \
    --template llama2 \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
    --output_dir models/llama2_7b_chat/llama-main/train_models/llama2_7b_chat_muti_gpus_01_epoch10/train_model \
    --overwrite_cache \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 2 \
    --lr_scheduler_type cosine \
    --logging_steps 10 \
    --save_steps 500 \
    --learning_rate 5e-5 \
    --num_train_epochs 100.0 \
    --plot_loss \
    --bf16 \
    --deepspeed models/llama2_7b_chat/llama-main/deepspeed_config/llama2_7b_chat_muti_gpus_01_epoch10.json

1. 测试模型

python src/cli_demo.py \
    --model_name_or_path models/llama2_7b_chat/origin_model \
    --template baichuan2 \
    --finetuning_type lora \
    --checkpoint_dir models/llama2_7b_chat/llama-main/train_models/llama2_7b_chat_muti_gpus_01_epoch10/train_model

6）启动服务

python src/web_demo1.py \
    --model_name_or_path models/llama2_7b_chat/origin_model/Llama-2-7b-chat-hf  \
    --template llama2 \
    --finetuning_type lora \
    --checkpoint_dir models/llama2_7b_chat/llama-main/train_models/llama2_7b_chat_muti_gpus_01_epoch10/train_model

3、注意事项：