长治市城乡建设局网站,自贡建设机械网站,怎么创建教育网站,网站建设实践报告小结ChatGLM2-6B微调实践-Lora方案 环境部署Lora微调项目部署准备数据集修改训练脚本adapter推理模型合并与量化合并后的模型推理 微调过程中遇到的问题参考#xff1a; 环境部署
申请阿里云GPU服务器#xff1a;
CentOS 7.6 64Anaconda3-2023.07-1-Linux-x86_64Python 3.11.5G… ChatGLM2-6B微调实践-Lora方案 环境部署Lora微调项目部署准备数据集修改训练脚本adapter推理模型合并与量化合并后的模型推理 微调过程中遇到的问题参考 环境部署
申请阿里云GPU服务器
CentOS 7.6 64Anaconda3-2023.07-1-Linux-x86_64Python 3.11.5GPU NVIDIA A10显存24 G/1 coreCPU 8 vCore/30G
安装Anaconda、CUDA、PyTorch 参考ChatGLM2-6B微调实践-P-Tuning方案
Lora微调
项目部署
git clone https://github.com/shuxueslpi/chatGLM-6B-QLoRA.git
cd chatGLM-6B-QLoRA
pip install -r requirements.txt准备数据集
准备我们自己的数据集分别生成训练文件和测试文件这两个文件放在项目data文件夹数据格式为
{instruction: 类型#裤*版型#宽松*风格#性感*图案#线条*裤型#阔腿裤, output: 宽松的阔腿裤这两年真的吸粉不少明星时尚达人的心头爱。毕竟好穿时尚谁都能穿出腿长2米的效果宽松的裤腿当然是遮肉小能手啊。上身随性自然不拘束面料亲肤舒适贴身体验感棒棒哒。系带部分增加设计看点还让单品的设计感更强。腿部线条若隐若现的性感撩人。颜色敲温柔的与裤子本身所呈现的风格有点反差萌。
}训练集文件 train.json 测试集文件 dev.json
修改训练脚本
创建train_qlora.sh添加以下命令
python3 train_qlora.py \
--train_args_json chatGLM_6B_QLoRA.json \
--model_name_or_path THUDM/chatglm-6b \
--train_data_path data/train.json \
--eval_data_path data/dev.json \
--lora_rank 4 \
--lora_dropout 0.05 \
--compute_dtype fp32修改model_name_or_path参数为本地真实的模型路径。 chatGLM_6B_QLoRA.json文件为所有transformers框架支持的TrainingArguments可根据实际情况自行修改。参考https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments
执行脚本训练训练过程如下
(base) [rootiZbp178u8rw9n9ko94ubbyZ chatGLM-6B-QLoRA-main]# sh train_qlora.sh BUG REPORT
Welcome to bitsandbytes. For bug reports, please runpython -m bitsandbytesand submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issuesbin /root/anaconda3/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda118.so
CUDA SETUP: CUDA runtime path found: /root/anaconda3/lib/libcudart.so.11.0
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 118
CUDA SETUP: Loading binary /root/anaconda3/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...
The model weights are not tied. Please use the tie_weights method before using the infer_auto_device function.
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:0700:00, 1.13s/it]
trainable params: 974,848 || all params: 3,389,286,400 || trainable%: 0.0287626327477076
Found cached dataset json (/root/.cache/huggingface/datasets/json/default-a26442a3257bd6e5/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0000:00, 920.81it/s]
Loading cached processed dataset at /root/.cache/huggingface/datasets/json/default-a26442a3257bd6e5/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-a6ee5e96ac795161.arrow
Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/json/default-a26442a3257bd6e5/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-20df68b061e7d292.arrow
Loading cached processed dataset at /root/.cache/huggingface/datasets/json/default-a26442a3257bd6e5/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-e9ff6a88c507a91d.arrow
Found cached dataset json (/root/.cache/huggingface/datasets/json/default-f82d1afe86c1e9ec/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)
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Loading cached processed dataset at /root/.cache/huggingface/datasets/json/default-f82d1afe86c1e9ec/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-baa6cdf34a027bbb.arrow
Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/json/default-f82d1afe86c1e9ec/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-8aa40269a670f4fd.arrow
Loading cached processed dataset at /root/.cache/huggingface/datasets/json/default-f82d1afe86c1e9ec/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-dd26c6462b17896e.arrow
wandb: Tracking run with wandb version 0.15.3
wandb: WB syncing is set to offline in this directory.
wandb: Run wandb online or set WANDB_MODEonline to enable cloud syncing.0%| | 0/160 [00:00?, ?it/s]use_cacheTrue is incompatible with gradient checkpointing. Setting use_cacheFalse...
/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 1.9799, learning_rate: 6.25e-05, epoch: 0.12}
{loss: 2.8439, learning_rate: 0.000125, epoch: 0.24}
{loss: 2.6293, learning_rate: 0.0001875, epoch: 0.35}
{loss: 2.6095, learning_rate: 0.00025, epoch: 0.47}
{loss: 2.2325, learning_rate: 0.0003125, epoch: 0.59}
{eval_loss: 2.7306337356567383, eval_runtime: 0.1659, eval_samples_per_second: 12.057, eval_steps_per_second: 12.057, epoch: 0.59} 3%|█████▉ | 5/160 [00:0401:36, 1.60it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 2.4916, learning_rate: 0.000375, epoch: 0.71}
{loss: 2.4591, learning_rate: 0.0004375, epoch: 0.82}
{loss: 2.0441, learning_rate: 0.0005, epoch: 0.94}
{loss: 1.8674, learning_rate: 0.0005625000000000001, epoch: 1.06}
{loss: 1.5093, learning_rate: 0.000625, epoch: 1.18}
{eval_loss: 1.626299262046814, eval_runtime: 0.1665, eval_samples_per_second: 12.013, eval_steps_per_second: 12.013, epoch: 1.18} 6%|███████████▉ | 10/160 [00:0601:23, 1.80it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 1.7075, learning_rate: 0.0006875, epoch: 1.29}
{loss: 1.6792, learning_rate: 0.00075, epoch: 1.41}
{loss: 1.4942, learning_rate: 0.0008125000000000001, epoch: 1.53}
{loss: 1.8202, learning_rate: 0.000875, epoch: 1.65}
{loss: 0.9729, learning_rate: 0.0009375, epoch: 1.76}
{eval_loss: 0.7719208002090454, eval_runtime: 0.1673, eval_samples_per_second: 11.953, eval_steps_per_second: 11.953, epoch: 1.76} 9%|█████████████████▊ | 15/160 [00:0901:20, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 1.3478, learning_rate: 0.001, epoch: 1.88}
{loss: 1.3449, learning_rate: 0.0009930555555555556, epoch: 2.0}
{loss: 0.6173, learning_rate: 0.0009861111111111112, epoch: 2.12}
{loss: 0.5325, learning_rate: 0.0009791666666666666, epoch: 2.24}
{loss: 1.1995, learning_rate: 0.0009722222222222222, epoch: 2.35}
{eval_loss: 0.06268511712551117, eval_runtime: 0.1694, eval_samples_per_second: 11.804, eval_steps_per_second: 11.804, epoch: 2.35} 12%|███████████████████████▊ | 20/160 [00:1201:17, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 1.0089, learning_rate: 0.0009652777777777778, epoch: 2.47}
{loss: 0.9793, learning_rate: 0.0009583333333333334, epoch: 2.59}
{loss: 0.814, learning_rate: 0.0009513888888888889, epoch: 2.71}
{loss: 1.1905, learning_rate: 0.0009444444444444445, epoch: 2.82}
{loss: 0.8011, learning_rate: 0.0009375, epoch: 2.94}
{eval_loss: 0.21860463917255402, eval_runtime: 0.1697, eval_samples_per_second: 11.782, eval_steps_per_second: 11.782, epoch: 2.94} 16%|█████████████████████████████▋ | 25/160 [00:1501:14, 1.82it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.3818, learning_rate: 0.0009305555555555556, epoch: 3.06}
{loss: 0.3961, learning_rate: 0.0009236111111111112, epoch: 3.18}
{loss: 0.4549, learning_rate: 0.0009166666666666666, epoch: 3.29}
{loss: 0.1871, learning_rate: 0.0009097222222222222, epoch: 3.41}
{loss: 0.8322, learning_rate: 0.0009027777777777778, epoch: 3.53}
{eval_loss: 0.11932770907878876, eval_runtime: 0.1681, eval_samples_per_second: 11.901, eval_steps_per_second: 11.901, epoch: 3.53} 19%|███████████████████████████████████▋ | 30/160 [00:1801:12, 1.80it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0794, learning_rate: 0.0008958333333333334, epoch: 3.65}
{loss: 0.2307, learning_rate: 0.0008888888888888888, epoch: 3.76}
{loss: 0.5759, learning_rate: 0.0008819444444444444, epoch: 3.88}
{loss: 0.5141, learning_rate: 0.000875, epoch: 4.0}
{loss: 0.1513, learning_rate: 0.0008680555555555556, epoch: 4.12}
{eval_loss: 0.01624702289700508, eval_runtime: 0.1677, eval_samples_per_second: 11.929, eval_steps_per_second: 11.929, epoch: 4.12} 22%|█████████████████████████████████████████▌ | 35/160 [00:2101:07, 1.84it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.1017, learning_rate: 0.0008611111111111112, epoch: 4.24}
{loss: 0.0328, learning_rate: 0.0008541666666666666, epoch: 4.35}
{loss: 0.03, learning_rate: 0.0008472222222222222, epoch: 4.47}
{loss: 0.1851, learning_rate: 0.0008402777777777778, epoch: 4.59}
{loss: 0.0946, learning_rate: 0.0008333333333333334, epoch: 4.71}
{eval_loss: 0.04633883014321327, eval_runtime: 0.1688, eval_samples_per_second: 11.845, eval_steps_per_second: 11.845, epoch: 4.71} 25%|███████████████████████████████████████████████▌ | 40/160 [00:2301:04, 1.87it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0802, learning_rate: 0.0008263888888888889, epoch: 4.82}
{loss: 0.5968, learning_rate: 0.0008194444444444445, epoch: 4.94}
{loss: 0.263, learning_rate: 0.0008125000000000001, epoch: 5.06}
{loss: 0.2971, learning_rate: 0.0008055555555555556, epoch: 5.18}
{loss: 0.0483, learning_rate: 0.0007986111111111112, epoch: 5.29}
{eval_loss: 0.18734805285930634, eval_runtime: 0.1696, eval_samples_per_second: 11.79, eval_steps_per_second: 11.79, epoch: 5.29} 28%|█████████████████████████████████████████████████████▍ | 45/160 [00:2601:04, 1.79it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0725, learning_rate: 0.0007916666666666666, epoch: 5.41}
{loss: 0.0611, learning_rate: 0.0007847222222222222, epoch: 5.53}
{loss: 0.0533, learning_rate: 0.0007777777777777778, epoch: 5.65}
{loss: 0.0112, learning_rate: 0.0007708333333333334, epoch: 5.76}
{loss: 0.085, learning_rate: 0.0007638888888888888, epoch: 5.88}
{eval_loss: 0.04521441087126732, eval_runtime: 0.1684, eval_samples_per_second: 11.877, eval_steps_per_second: 11.877, epoch: 5.88} 31%|███████████████████████████████████████████████████████████▍ | 50/160 [00:2901:00, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0027, learning_rate: 0.0007569444444444444, epoch: 6.0}
{loss: 0.0903, learning_rate: 0.00075, epoch: 6.12}
{loss: 0.0194, learning_rate: 0.0007430555555555556, epoch: 6.24}
{loss: 0.1515, learning_rate: 0.0007361111111111112, epoch: 6.35}
{loss: 0.1667, learning_rate: 0.0007291666666666666, epoch: 6.47}
{eval_loss: 0.010895016603171825, eval_runtime: 0.1706, eval_samples_per_second: 11.722, eval_steps_per_second: 11.722, epoch: 6.47} 34%|█████████████████████████████████████████████████████████████████▎ | 55/160 [00:3200:58, 1.80it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.1637, learning_rate: 0.0007222222222222222, epoch: 6.59}
{loss: 0.0302, learning_rate: 0.0007152777777777778, epoch: 6.71}
{loss: 0.2117, learning_rate: 0.0007083333333333334, epoch: 6.82}
{loss: 0.0893, learning_rate: 0.0007013888888888889, epoch: 6.94}
{loss: 0.0446, learning_rate: 0.0006944444444444445, epoch: 7.06}
{eval_loss: 0.017673835158348083, eval_runtime: 0.1683, eval_samples_per_second: 11.883, eval_steps_per_second: 11.883, epoch: 7.06} 38%|███████████████████████████████████████████████████████████████████████▎ | 60/160 [00:3500:54, 1.82it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0124, learning_rate: 0.0006875, epoch: 7.18}
{loss: 0.0157, learning_rate: 0.0006805555555555556, epoch: 7.29}
{loss: 0.0426, learning_rate: 0.0006736111111111112, epoch: 7.41}
{loss: 0.1031, learning_rate: 0.0006666666666666666, epoch: 7.53}
{loss: 0.0127, learning_rate: 0.0006597222222222222, epoch: 7.65}
{eval_loss: 0.04288101941347122, eval_runtime: 0.1691, eval_samples_per_second: 11.826, eval_steps_per_second: 11.826, epoch: 7.65} 41%|█████████████████████████████████████████████████████████████████████████████▏ | 65/160 [00:3800:52, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0545, learning_rate: 0.0006527777777777778, epoch: 7.76}
{loss: 0.0147, learning_rate: 0.0006458333333333334, epoch: 7.88}
{loss: 0.0342, learning_rate: 0.0006388888888888888, epoch: 8.0}
{loss: 0.0109, learning_rate: 0.0006319444444444444, epoch: 8.12}
{loss: 0.003, learning_rate: 0.000625, epoch: 8.24}
{eval_loss: 0.003974442835897207, eval_runtime: 0.1712, eval_samples_per_second: 11.685, eval_steps_per_second: 11.685, epoch: 8.24} 44%|███████████████████████████████████████████████████████████████████████████████████▏ | 70/160 [00:4100:49, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0151, learning_rate: 0.0006180555555555556, epoch: 8.35}
{loss: 0.0043, learning_rate: 0.0006111111111111112, epoch: 8.47}
{loss: 0.0145, learning_rate: 0.0006041666666666666, epoch: 8.59}
{loss: 0.0067, learning_rate: 0.0005972222222222222, epoch: 8.71}
{loss: 0.005, learning_rate: 0.0005902777777777778, epoch: 8.82}
{eval_loss: 0.004422146826982498, eval_runtime: 0.17, eval_samples_per_second: 11.762, eval_steps_per_second: 11.762, epoch: 8.82} 47%|█████████████████████████████████████████████████████████████████████████████████████████ | 75/160 [00:4400:47, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0384, learning_rate: 0.0005833333333333334, epoch: 8.94}
{loss: 0.003, learning_rate: 0.0005763888888888889, epoch: 9.06}
{loss: 0.0035, learning_rate: 0.0005694444444444445, epoch: 9.18}
{loss: 0.0029, learning_rate: 0.0005625000000000001, epoch: 9.29}
{loss: 0.0027, learning_rate: 0.0005555555555555556, epoch: 9.41}
{eval_loss: 0.0012616427848115563, eval_runtime: 0.1692, eval_samples_per_second: 11.82, eval_steps_per_second: 11.82, epoch: 9.41} 50%|███████████████████████████████████████████████████████████████████████████████████████████████ | 80/160 [00:4600:43, 1.85it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0105, learning_rate: 0.0005486111111111112, epoch: 9.53}
{loss: 0.0019, learning_rate: 0.0005416666666666666, epoch: 9.65}
{loss: 0.0058, learning_rate: 0.0005347222222222222, epoch: 9.76}
{loss: 0.0027, learning_rate: 0.0005277777777777778, epoch: 9.88}
{loss: 0.0236, learning_rate: 0.0005208333333333334, epoch: 10.0}
{eval_loss: 0.0005044374847784638, eval_runtime: 0.1711, eval_samples_per_second: 11.691, eval_steps_per_second: 11.691, epoch: 10.0} 53%|████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 85/160 [00:4900:41, 1.79it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0024, learning_rate: 0.0005138888888888888, epoch: 10.12}
{loss: 0.0009, learning_rate: 0.0005069444444444444, epoch: 10.24}
{loss: 0.0009, learning_rate: 0.0005, epoch: 10.35}
{loss: 0.0024, learning_rate: 0.0004930555555555556, epoch: 10.47}
{loss: 0.0022, learning_rate: 0.0004861111111111111, epoch: 10.59}
{eval_loss: 0.00045453320490196347, eval_runtime: 0.1717, eval_samples_per_second: 11.646, eval_steps_per_second: 11.646, epoch: 10.59} 56%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 90/160 [00:5200:38, 1.80it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0014, learning_rate: 0.0004791666666666667, epoch: 10.71}
{loss: 0.0009, learning_rate: 0.00047222222222222224, epoch: 10.82}
{loss: 0.0047, learning_rate: 0.0004652777777777778, epoch: 10.94}
{loss: 0.0017, learning_rate: 0.0004583333333333333, epoch: 11.06}
{loss: 0.0011, learning_rate: 0.0004513888888888889, epoch: 11.18}
{eval_loss: 0.0004512905434239656, eval_runtime: 0.1693, eval_samples_per_second: 11.816, eval_steps_per_second: 11.816, epoch: 11.18} 59%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 95/160 [00:5500:35, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.001, learning_rate: 0.0004444444444444444, epoch: 11.29}
{loss: 0.0006, learning_rate: 0.0004375, epoch: 11.41}
{loss: 0.0006, learning_rate: 0.0004305555555555556, epoch: 11.53}
{loss: 0.0015, learning_rate: 0.0004236111111111111, epoch: 11.65}
{loss: 0.0035, learning_rate: 0.0004166666666666667, epoch: 11.76}
{eval_loss: 0.00042770570144057274, eval_runtime: 0.1702, eval_samples_per_second: 11.75, eval_steps_per_second: 11.75, epoch: 11.76} 62%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 100/160 [00:5800:34, 1.76it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0008, learning_rate: 0.00040972222222222224, epoch: 11.88}
{loss: 0.0013, learning_rate: 0.0004027777777777778, epoch: 12.0}
{loss: 0.0005, learning_rate: 0.0003958333333333333, epoch: 12.12}
{loss: 0.0006, learning_rate: 0.0003888888888888889, epoch: 12.24}
{loss: 0.0004, learning_rate: 0.0003819444444444444, epoch: 12.35}
{eval_loss: 0.0004152616602368653, eval_runtime: 0.1709, eval_samples_per_second: 11.704, eval_steps_per_second: 11.704, epoch: 12.35} 66%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 105/160 [01:0100:29, 1.84it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0004, learning_rate: 0.000375, epoch: 12.47}
{loss: 0.0014, learning_rate: 0.0003680555555555556, epoch: 12.59}
{loss: 0.0004, learning_rate: 0.0003611111111111111, epoch: 12.71}
{loss: 0.001, learning_rate: 0.0003541666666666667, epoch: 12.82}
{loss: 0.0011, learning_rate: 0.00034722222222222224, epoch: 12.94}
{eval_loss: 0.0003934216219931841, eval_runtime: 0.1709, eval_samples_per_second: 11.706, eval_steps_per_second: 11.706, epoch: 12.94} 69%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 110/160 [01:0400:28, 1.77it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0003, learning_rate: 0.0003402777777777778, epoch: 13.06}
{loss: 0.0011, learning_rate: 0.0003333333333333333, epoch: 13.18}
{loss: 0.0004, learning_rate: 0.0003263888888888889, epoch: 13.29}
{loss: 0.0004, learning_rate: 0.0003194444444444444, epoch: 13.41}
{loss: 0.0005, learning_rate: 0.0003125, epoch: 13.53}
{eval_loss: 0.0003844855527859181, eval_runtime: 0.171, eval_samples_per_second: 11.694, eval_steps_per_second: 11.694, epoch: 13.53} 72%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 115/160 [01:0700:24, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0004, learning_rate: 0.0003055555555555556, epoch: 13.65}
{loss: 0.0007, learning_rate: 0.0002986111111111111, epoch: 13.76}
{loss: 0.0004, learning_rate: 0.0002916666666666667, epoch: 13.88}
{loss: 0.0006, learning_rate: 0.00028472222222222223, epoch: 14.0}
{loss: 0.0007, learning_rate: 0.0002777777777777778, epoch: 14.12}
{eval_loss: 0.00029086240101605654, eval_runtime: 0.1713, eval_samples_per_second: 11.673, eval_steps_per_second: 11.673, epoch: 14.12} 75%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 120/160 [01:0900:22, 1.78it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0004, learning_rate: 0.0002708333333333333, epoch: 14.24}
{loss: 0.0004, learning_rate: 0.0002638888888888889, epoch: 14.35}
{loss: 0.0007, learning_rate: 0.0002569444444444444, epoch: 14.47}
{loss: 0.0003, learning_rate: 0.00025, epoch: 14.59}
{loss: 0.0003, learning_rate: 0.00024305555555555555, epoch: 14.71}
{eval_loss: 0.00020883249817416072, eval_runtime: 0.1722, eval_samples_per_second: 11.611, eval_steps_per_second: 11.611, epoch: 14.71} 78%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 125/160 [01:1200:19, 1.81it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0008, learning_rate: 0.00023611111111111112, epoch: 14.82}
{loss: 0.0002, learning_rate: 0.00022916666666666666, epoch: 14.94}
{loss: 0.0003, learning_rate: 0.0002222222222222222, epoch: 15.06}
{loss: 0.0002, learning_rate: 0.0002152777777777778, epoch: 15.18}
{loss: 0.0005, learning_rate: 0.00020833333333333335, epoch: 15.29}
{eval_loss: 0.00018148773233406246, eval_runtime: 0.172, eval_samples_per_second: 11.63, eval_steps_per_second: 11.63, epoch: 15.29} 81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 130/160 [01:1500:16, 1.79it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0002, learning_rate: 0.0002013888888888889, epoch: 15.41}
{loss: 0.0002, learning_rate: 0.00019444444444444446, epoch: 15.53}
{loss: 0.0008, learning_rate: 0.0001875, epoch: 15.65}
{loss: 0.0004, learning_rate: 0.00018055555555555555, epoch: 15.76}
{loss: 0.0005, learning_rate: 0.00017361111111111112, epoch: 15.88}
{eval_loss: 0.00016719780978746712, eval_runtime: 0.1707, eval_samples_per_second: 11.716, eval_steps_per_second: 11.716, epoch: 15.88} 84%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 135/160 [01:1800:13, 1.79it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0002, learning_rate: 0.00016666666666666666, epoch: 16.0}
{loss: 0.0002, learning_rate: 0.0001597222222222222, epoch: 16.12}
{loss: 0.0002, learning_rate: 0.0001527777777777778, epoch: 16.24}
{loss: 0.0003, learning_rate: 0.00014583333333333335, epoch: 16.35}
{loss: 0.0004, learning_rate: 0.0001388888888888889, epoch: 16.47}
{eval_loss: 0.00015975727001205087, eval_runtime: 0.1705, eval_samples_per_second: 11.728, eval_steps_per_second: 11.728, epoch: 16.47} 88%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 140/160 [01:2100:11, 1.80it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0002, learning_rate: 0.00013194444444444446, epoch: 16.59}
{loss: 0.0007, learning_rate: 0.000125, epoch: 16.71}
{loss: 0.0002, learning_rate: 0.00011805555555555556, epoch: 16.82}
{loss: 0.0005, learning_rate: 0.0001111111111111111, epoch: 16.94}
{loss: 0.0005, learning_rate: 0.00010416666666666667, epoch: 17.06}
{eval_loss: 0.00015415673260577023, eval_runtime: 0.1704, eval_samples_per_second: 11.737, eval_steps_per_second: 11.737, epoch: 17.06} 91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 145/160 [01:2400:08, 1.76it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0001, learning_rate: 9.722222222222223e-05, epoch: 17.18}
{loss: 0.0002, learning_rate: 9.027777777777777e-05, epoch: 17.29}
{loss: 0.0003, learning_rate: 8.333333333333333e-05, epoch: 17.41}
{loss: 0.0002, learning_rate: 7.63888888888889e-05, epoch: 17.53}
{loss: 0.0007, learning_rate: 6.944444444444444e-05, epoch: 17.65}
{eval_loss: 0.00015087085193954408, eval_runtime: 0.1715, eval_samples_per_second: 11.662, eval_steps_per_second: 11.662, epoch: 17.65} 94%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 150/160 [01:2700:05, 1.77it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0002, learning_rate: 6.25e-05, epoch: 17.76}
{loss: 0.0003, learning_rate: 5.555555555555555e-05, epoch: 17.88}
{loss: 0.0004, learning_rate: 4.8611111111111115e-05, epoch: 18.0}
{loss: 0.0007, learning_rate: 4.1666666666666665e-05, epoch: 18.12}
{loss: 0.0002, learning_rate: 3.472222222222222e-05, epoch: 18.24}
{eval_loss: 0.00014878850197419524, eval_runtime: 0.1731, eval_samples_per_second: 11.554, eval_steps_per_second: 11.554, epoch: 18.24} 97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 155/160 [01:3000:02, 1.79it/s/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrantTrue or use_reentrantFalse explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrantTrue. It is recommended that you use use_reentrantFalse. Refer to docs for more details on the differences between the two variants.warnings.warn(
{loss: 0.0001, learning_rate: 2.7777777777777776e-05, epoch: 18.35}
{loss: 0.0002, learning_rate: 2.0833333333333333e-05, epoch: 18.47}
{loss: 0.0003, learning_rate: 1.3888888888888888e-05, epoch: 18.59}
{loss: 0.0005, learning_rate: 6.944444444444444e-06, epoch: 18.71}
{loss: 0.0004, learning_rate: 0.0, epoch: 18.82}
{eval_loss: 0.00014794316666666418, eval_runtime: 0.1724, eval_samples_per_second: 11.602, eval_steps_per_second: 11.602, epoch: 18.82}
{train_runtime: 99.4155, train_samples_per_second: 3.42, train_steps_per_second: 1.609, train_loss: 0.2966294680955798, epoch: 18.82}
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 160/160 [01:3300:00, 1.72it/s]
wandb: Waiting for WB process to finish... (success).
wandb:
wandb: Run history:
wandb: eval/loss █▅▃▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: eval/runtime ▁▂▂▄▅▃▃▄▅▃▆▃▄▆▅▄▆▇▄▅▆▆▆▆▇▇▆▅▅▆█▇
wandb: eval/samples_per_second █▇▇▄▄▆▆▅▄▅▃▆▅▃▄▅▃▂▅▄▃▃▃▃▂▂▃▃▄▃▁▂
wandb: eval/steps_per_second █▇▇▄▄▆▆▅▄▅▃▆▅▃▄▅▃▂▅▄▃▃▃▃▂▂▃▃▄▃▁▂
wandb: train/epoch ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb: train/learning_rate ▂▄▅▇███▇▇▇▇▇▆▆▆▆▆▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▂▂▂▂▂▁▁▁
wandb: train/loss █▇▅▅▃▃▂▃▂▁▂▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: train/total_flos ▁
wandb: train/train_loss ▁
wandb: train/train_runtime ▁
wandb: train/train_samples_per_second ▁
wandb: train/train_steps_per_second ▁
wandb:
wandb: Run summary:
wandb: eval/loss 0.00015
wandb: eval/runtime 0.1724
wandb: eval/samples_per_second 11.602
wandb: eval/steps_per_second 11.602
wandb: train/epoch 18.82
wandb: train/global_step 160
wandb: train/learning_rate 0.0
wandb: train/loss 0.0004
wandb: train/total_flos 293076187017216.0
wandb: train/train_loss 0.29663
wandb: train/train_runtime 99.4155
wandb: train/train_samples_per_second 3.42
wandb: train/train_steps_per_second 1.609
wandb:
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /root/chatGLM-6B-QLoRA-main/wandb/offline-run-20231010_100222-9j69u4g5
wandb: Find logs at: ./wandb/offline-run-20231010_100222-9j69u4g5/logs训练完成会在saved_files文件夹下生成checkpoint及adapter文件。
adapter推理
创建test.py脚本添加以下代码
import torch
from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel, PeftConfigpeft_model_path saved_files/chatGLM_6B_QLoRA_t32config PeftConfig.from_pretrained(peft_model_path)
q_config BitsAndBytesConfig(load_in_4bitTrue,bnb_4bit_quant_typenf4,bnb_4bit_use_double_quantTrue,bnb_4bit_compute_dtypetorch.float32)base_model AutoModel.from_pretrained(config.base_model_name_or_path,quantization_configq_config,trust_remote_codeTrue,device_mapauto)input_text 配网故障故障评价的指标是什么
print(f输入\n{input_text})
tokenizer AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_codeTrue)response, history base_model.chat(tokenizertokenizer, queryinput_text)
print(f微调前\n{response})model PeftModel.from_pretrained(base_model, peft_model_path)
response, history model.chat(tokenizertokenizer, queryinput_text)
print(f微调后: \n{response})推理结果如下 通过对比微调前和微调后的回答结果可见微调后的回答更贴近数据集的内容 模型微调达到了预期目标。
模型合并与量化
合并微调的Lora model和基座base model。
使用的项目目前使用的peft为dev的版本在合并lora model和base model时会报错。项目推荐将peft的版本回退到0.3.0。
pip install peft0.3.0创建merge_lora_and_quantize.sh脚本添加如下命令
python3 merge_lora_and_quantize.py \
--lora_path saved_files/chatGLM_6B_QLoRA_t32 \
--output_path /tmp/merged_qlora_model_4bit \
--remote_scripts_dir /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b \
--qbits 4remote_scripts_dir 为ChatGLM2的本地地址 qbits 量化为4bit 执行脚本运行结果如下
(base) [rootiZbp178u8rw9n9ko94ubbyZ chatGLM-6B-QLoRA-main]# sh merge_lora_and_quantize.sh BUG REPORT
Welcome to bitsandbytes. For bug reports, please runpython -m bitsandbytesand submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issuesbin /root/anaconda3/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda118.so
CUDA SETUP: CUDA runtime path found: /root/anaconda3/lib/libcudart.so.11.0
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 118
CUDA SETUP: Loading binary /root/anaconda3/lib/python3.11/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...
The model weights are not tied. Please use the tie_weights method before using the infer_auto_device function.
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:0900:00, 1.40s/it]
2023-10-10 10:39:59.215 | INFO | __main__:main:58 - Lora model和base model成功merge, 并量化为4bits, 保存在/tmp/merged_qlora_model_4bit模型合并的如下所示
合并后的模型推理
创建test2.py添加如下代码
from transformers import AutoModel, AutoTokenizermodel_path /tmp/merged_qlora_model_4bittokenizer AutoTokenizer.from_pretrained(model_path, trust_remote_codeTrue)
model AutoModel.from_pretrained(model_path, trust_remote_codeTrue).half().cuda()input_text 配网故障故障评价的指标是什么
response, history model.chat(tokenizertokenizer, queryinput_text)
print(response)推理结果如下贴合训练数据集的内容
测试lora微调后是否会产生灾难性遗忘问题
1、以李白的风格写一首描述秋天的七言律诗 2、开车过程如何减少燃油消耗 测试没有出现类似P-Tuning微调出现的严重遗忘问题。
微调过程中遇到的问题
1、出现wandb让选择WB Account 解决方法 1、执行wandb off关掉即可 2、在train_qlora.py开头配置环境变量 os.environ[“WANDB_DISABLED”] “true” 2、微调后saved_files中只有adapter_config.json和adapter_model.bin两个文件没有checkpoint 正常训练完不只这两个文件还会有多个checkpoints。原因是训练的步数太少连保存checkpoint的step都没有达到。
解决方法 适当调整chatGLM_6B_QLoRA.json中的配置 参考
https://github.com/shuxueslpi/chatGLM-6B-QLoRA https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments
