chore: ss

config.py

@@ -4,17 +4,17 @@ from dataclasses import dataclass, field
 @dataclass
 class DataConfig:
     template: str = """Translate this Chinese text to English:
-{input}
+{}
 ---
 Translation:
-{output}"""
+{}"""
     train_split: float = 0.9
-    max_samples: int | None = None
+    max_samples: int | None = 3000
 
 
 @dataclass
 class WandBConfig:
-    enabled: bool = True
+    enabled: bool = False
     project: str | None = None
     name: str | None = None
     entity: str | None = None
@@ -34,8 +34,8 @@ class TrainingConfig:
     load_in_4bit: bool = False
 
     # LoRA
-    lora_r: int = 256
-    lora_alpha: int = 512
+    lora_r: int = 64
+    lora_alpha: int = 128
     lora_dropout: float = 0
     target_modules: list[str] = field(
         default_factory=lambda: [
@@ -56,7 +56,7 @@ class TrainingConfig:
     # training args
     per_device_train_batch_size: int = 16
     gradient_accumulation_steps: int = 2
-    warmup_ratio: float = 0.03
+    warmup_ratio: float = 0.05
     max_grad_norm: float = 1.0
     num_train_epochs: float = 1
     learning_rate: float = 5e-4
@@ -74,11 +74,11 @@ class TrainingConfig:
     packing: bool = True
 
     # eval
-    fp16_full_eval = True
-    per_device_eval_batch_size = 2
-    eval_accumulation_steps = 4
-    eval_strategy = "steps"
-    eval_steps = 1
+    fp16_full_eval: bool = True
+    per_device_eval_batch_size: int = 16
+    eval_accumulation_steps: int = 4
+    eval_strategy: str = "steps"
+    eval_steps: int = 5
 
     # output
     output_dir: str = "/workspace/output/"