LLama factory接入wandb面板
https://i-blog.csdnimg.cn/direct/7fe7ff62885146368ba917870c97e280.pnghttps://github.com/hiyouga/LLaMA-Factory/blob/main/README_zh.md
step1：先自己在wandb注册一个账号，拿到key
详见：WandB 简明教程【Weights & Bias】
step2：开始设置
阐明！！我是在代码行里运行有问题，所以直接演示网页端
[*]webui起来后，举行设置，wandb是在这里
https://i-blog.csdnimg.cn/direct/2bc8d5c777ec4c2b862070e3a5a0f719.png
[*]然后预览下令 会出现一个report_to: all就对了，然后直接运行
llamafactory-cli train \
--stage sft \
--do_train True \
--model_name_or_path {模型路径} \
--preprocessing_num_workers 16 \
--finetuning_type lora \
--template llama3 \
--flash_attn auto \
--dataset_dir data \
--dataset {数据集路径} \
--cutoff_len 1024 \
--learning_rate 5e-05 \
--num_train_epochs 3.0 \
--max_samples 100000 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--max_grad_norm 1.0 \
--logging_steps 5 \
--save_steps 100 \
--warmup_steps 0 \
--optim adamw_torch \
--packing False \
--report_to all \
--output_dir {输出文件路径} \
--bf16 True \
--plot_loss True \
--ddp_timeout 180000000 \
--include_num_input_tokens_seen True \
--lora_rank 8 \
--lora_alpha 16 \
--lora_dropout 0 \
--lora_target all
[*]运行到最后让你选择w&b使用模式，选2，回车
wandb: Using wandb-core as the SDK backend.Please refer to https://wandb.me/wandb-core for more information.
wandb: (1) Create a W&B account
wandb: (2) Use an existing W&B account
wandb: (3) Don't visualize my results
wandb: Enter your choice: 2
[*]输入你在官网拿的的key即可
wandb: You chose 'Use an existing W&B account'
wandb: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)
wandb: You can find your API key in your browser here: https://wandb.ai/authorize
wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:
[*]最后点
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