fix lint (#856)

* fix lint * fix lint
InternLM · Jul 19, 2024 · f49ac98 · f49ac98
1 parent 27cf856
commit f49ac98
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Showing 7 changed files with 17 additions and 17 deletions.
diff --git a/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm_1b_full_custom_pretrain_e1.py b/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm_1b_full_custom_pretrain_e1.py
@@ -36,7 +36,7 @@
 use_varlen_attn = False
 
 # Data
-data_files = ['/root/ld/pull_request/xtuner/xtuner/configs/custom_dataset/pretrain/minicpm/pretrain.json']
+data_files = ['/path/to/json/file.json']
 max_length = 2048
 pack_to_max_length = True
 

diff --git a/xtuner/configs/custom_dataset/sft/minicpm/minicpm_1b_full_custom_pretrain_e1.py b/xtuner/configs/custom_dataset/sft/minicpm/minicpm_1b_full_custom_pretrain_e1.py
@@ -36,7 +36,7 @@
 use_varlen_attn = False
 
 # Data
-data_files = ['/root/ld/pull_request/xtuner/xtuner/configs/custom_dataset/pretrain/minicpm/pretrain.json']
+data_files = ['/path/to/json/file.json']
 max_length = 2048
 pack_to_max_length = True
 

diff --git a/xtuner/configs/minicpm/1_2b/minicpm_1b_dpo_qlora.py b/xtuner/configs/minicpm/1_2b/minicpm_1b_dpo_qlora.py
@@ -1,13 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from datasets import load_dataset
 import torch
+from datasets import load_dataset
 from mmengine.dataset import DefaultSampler
 from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                             LoggerHook, ParamSchedulerHook)
 from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
-from torch.optim import AdamW
-from transformers import AutoModelForCausalLM, AutoTokenizer,BitsAndBytesConfig
 from peft import LoraConfig
+from torch.optim import AdamW
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig)
+
 from xtuner.dataset.collate_fns.preference_collate_fn import \
     preference_collate_fn
 from xtuner.dataset.preference_dataset import (build_preference_dataset,

diff --git a/xtuner/configs/minicpm/1_2b/minicpm_1b_lora_alpaca_zh_e3.py b/xtuner/configs/minicpm/1_2b/minicpm_1b_lora_alpaca_zh_e3.py
@@ -7,8 +7,7 @@
 from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
 from peft import LoraConfig
 from torch.optim import AdamW
-from transformers import (AutoModelForCausalLM, AutoTokenizer,
-                          BitsAndBytesConfig)
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from xtuner.dataset import process_hf_dataset
 from xtuner.dataset.collate_fns import default_collate_fn
@@ -78,7 +77,7 @@
         pretrained_model_name_or_path=pretrained_model_name_or_path,
         trust_remote_code=True,
         torch_dtype=torch.float16,
-        ),
+    ),
     lora=dict(
         type=LoraConfig,
         r=64,

diff --git a/xtuner/configs/minicpm/2b/minicpm_2b_dpo_qlora.py b/xtuner/configs/minicpm/2b/minicpm_2b_dpo_qlora.py
@@ -1,13 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from datasets import load_dataset
 import torch
+from datasets import load_dataset
 from mmengine.dataset import DefaultSampler
 from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                             LoggerHook, ParamSchedulerHook)
 from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
-from torch.optim import AdamW
-from transformers import AutoModelForCausalLM, AutoTokenizer,BitsAndBytesConfig
 from peft import LoraConfig
+from torch.optim import AdamW
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig)
+
 from xtuner.dataset.collate_fns.preference_collate_fn import \
     preference_collate_fn
 from xtuner.dataset.preference_dataset import (build_preference_dataset,

diff --git a/xtuner/configs/minicpm/2b/minicpm_2b_lora_alpaca_zh_e3.py b/xtuner/configs/minicpm/2b/minicpm_2b_lora_alpaca_zh_e3.py
@@ -7,8 +7,7 @@
 from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
 from peft import LoraConfig
 from torch.optim import AdamW
-from transformers import (AutoModelForCausalLM, AutoTokenizer,
-                          BitsAndBytesConfig)
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from xtuner.dataset import process_hf_dataset
 from xtuner.dataset.collate_fns import default_collate_fn
@@ -78,7 +77,7 @@
         pretrained_model_name_or_path=pretrained_model_name_or_path,
         trust_remote_code=True,
         torch_dtype=torch.float16,
-        ),
+    ),
     lora=dict(
         type=LoraConfig,
         r=64,

diff --git a/xtuner/utils/templates.py b/xtuner/utils/templates.py
@@ -130,9 +130,7 @@
         SYSTEM=('[INST] {system} [/INST]\n'),
         INSTRUCTION=('[INST] {input} [/INST]'),
         SEP='\n'),
-    minicpm=dict(
-        INSTRUCTION=('<用户> {input} <AI>'),
-        SEP='\n'),
+    minicpm=dict(INSTRUCTION=('<用户> {input} <AI>'), SEP='\n'),
     gemma=dict(
         # `system` field is extended by xtuner
         SYSTEM=('<start_of_turn>system\n{system}<end_of_turn>\n'),