lmdeploy 部署量化出现错误

[bestkojima]

lmdeploy lite auto_awq
/root/models/internlm2_5-1_8b-chat
--calib-dataset 'ptb'
--calib-samples 128
--calib-seqlen 2048
--w-bits 4
--w-group-size 128
--batch-size 1
--search-scale False
--work-dir /root/models/internlm2_5-1_8b-chat-w4a16-4bit
输入此命令后
报错显示

Traceback (most recent call last):
File "/root/.conda/envs/lmdeploy/bin/lmdeploy", line 8, in <module>
  sys.exit(run())
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/cli/entrypoint.py", line 36, in run
  args.run(args)
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/cli/lite.py", line 139, in auto_awq
  auto_awq(**kwargs)
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/lite/apis/auto_awq.py", line 80, in auto_awq
  vl_model, model, tokenizer, work_dir = calibrate(model,
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/lite/apis/calibrate.py", line 216, in calibrate
  calib_loader, _ = get_calib_loaders(calib_dataset,
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/lite/utils/calib_dataloader.py", line 302, in get_calib_loaders
  return get_ptb(tokenizer, nsamples, seed, seqlen)
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/lite/utils/calib_dataloader.py", line 58, in get_ptb
  traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/datasets/load.py", line 2074, in load_dataset
  builder_instance = load_dataset_builder(
File "/root/.conda/envs/lmdeploy/lib/python3.10/site-packages/datasets/load.py", line 1832, in load_dataset_builder
  builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable

如何解决？

[bestkojima]

已解决 原因是最新的datasets3.0 无法下载calibrate数据集
输入 pip index versions datasets
找到离最新的最近的一个版本进行下载即可

[JunyaoHu]

非常感谢，对我有用，我选择的版本是 2.19.2

Available versions: 3.0.1, 3.0.0, 2.21.0, 2.20.0, 2.19.2, 2.19.1, 
2.19.0, 2.18.0, 2.17.1, 2.17.0, 2.16.1, 2.16.0, 2.15.0, 2.14.7, 
2.14.6, 2.14.5, 2.14.4, 2.14.3, 2.14.2, 2.14.1, 2.14.0, 2.13.2, 
2.13.1, 2.13.0, 2.12.0, 2.11.0, 2.10.1, 2.10.0, 2.9.0, 2.8.0, 
2.7.1, 2.7.0, 2.6.2, 2.6.1, 2.6.0, 2.5.2, 2.5.1, 2.5.0, 2.4.0, 
2.3.2, 2.3.1, 2.3.0, 2.2.2, 2.2.1, 2.2.0, 2.1.0, 2.0.0, 1.18.4, 
1.18.3, 1.18.2, 1.18.1, 1.18.0, 1.17.0, 1.16.1, 1.16.0, 1.15.1, 
1.15.0, 1.14.0, 1.13.3, 1.13.2, 1.13.1, 1.13.0, 1.12.1, 1.12.0, 
1.11.0, 1.10.2, 1.10.1, 1.10.0, 1.9.0, 1.8.0, 1.7.0, 1.6.2, 1.6.1, 
1.6.0, 1.5.0, 1.4.1, 1.4.0, 1.3.0, 1.2.1, 1.2.0, 1.1.3, 1.1.2, 
1.1.1, 1.1.0, 1.0.2, 1.0.1, 1.0.0, 0.0.9

pip install datasets==2.19.2

测试可行