Typos, type error

Author: Arsh Zahed

src/together/cli/api/finetune.py

@@ -87,13 +87,13 @@ def fine_tuning(ctx: click.Context) -> None:
     "--num-cycles",
     type=float,
     default=0.5,
-    help="Number of cycles for cosine learning rate scheduler.",
+    help="Number of cycles for the cosine learning rate scheduler.",
 )
 @click.option(
     "--warmup-ratio",
     type=float,
     default=0.0,
-    help="Warmup ratio for learning rate scheduler.",
+    help="Warmup ratio for the learning rate scheduler.",
 )
 @click.option(
     "--max-grad-norm",

src/together/resources/finetune.py

@@ -298,8 +298,8 @@ def create(
             lr_scheduler_type (Literal["linear", "cosine"]): Learning rate scheduler type. Defaults to "linear".
             min_lr_ratio (float, optional): Min learning rate ratio of the initial learning rate for
                 the learning rate scheduler. Defaults to 0.0.
-            num_cycles (float, optional): Number of cycles for cosine learning rate scheduler. Defaults to 0.5.
-            warmup_ratio (float, optional): Warmup ratio for learning rate scheduler.
+            num_cycles (float, optional): Number of cycles for the cosine learning rate scheduler. Defaults to 0.5.
+            warmup_ratio (float, optional): Warmup ratio for the learning rate scheduler.
             max_grad_norm (float, optional): Max gradient norm. Defaults to 1.0, set to 0 to disable.
             weight_decay (float, optional): Weight decay. Defaults to 0.0.
             lora (bool, optional): Whether to use LoRA adapters. Defaults to True.
@@ -677,8 +677,8 @@ async def create(
             lr_scheduler_type (Literal["linear", "cosine"]): Learning rate scheduler type. Defaults to "linear".
             min_lr_ratio (float, optional): Min learning rate ratio of the initial learning rate for
                 the learning rate scheduler. Defaults to 0.0.
-            num_cycles (float, optional): Number of cycles for cosine learning rate scheduler. Defaults to 0.5.
-            warmup_ratio (float, optional): Warmup ratio for learning rate scheduler.
+            num_cycles (float, optional): Number of cycles for the cosine learning rate scheduler. Defaults to 0.5.
+            warmup_ratio (float, optional): Warmup ratio for the learning rate scheduler.
             max_grad_norm (float, optional): Max gradient norm. Defaults to 1.0, set to 0 to disable.
             weight_decay (float, optional): Weight decay. Defaults to 0.0.
             lora (bool, optional): Whether to use LoRA adapters. Defaults to True.

src/together/types/finetune.py

@@ -370,28 +370,28 @@ class FinetuneLRScheduler(BaseModel):
 
     @field_validator("lr_scheduler_type")
     @classmethod
-    def validate_scheduler_type(cls, v: str) -> str:
-        if v not in LRSchedulerTypeToArgs:
+    def validate_scheduler_type(cls, scheduler_type: str) -> str:
+        if scheduler_type not in LRSchedulerTypeToArgs:
             raise ValueError(
                 f"Scheduler type must be one of: {LRSchedulerTypeToArgs.keys()}"
             )
-        return v
+        return scheduler_type
 
     @field_validator("lr_scheduler_args")
     @classmethod
     def validate_scheduler_args(
-        cls, v: FinetuneLRSchedulerArgs, info: ValidationInfo
+        cls, args: FinetuneLRSchedulerArgs, info: ValidationInfo
     ) -> FinetuneLRSchedulerArgs:
         scheduler_type = str(info.data.get("lr_scheduler_type"))
 
-        if v is None:
-            return v
+        if args is None:
+            return args
 
         expected_type = LRSchedulerTypeToArgs[scheduler_type]
-        if not isinstance(v, expected_type):
-            raise ValueError(f"Expected {expected_type}, got {type(v)}")
+        if not isinstance(args, expected_type):
+            raise TypeError(f"Expected {expected_type}, got {type(args)}")
 
-        return v
+        return args
 
 
 class FinetuneCheckpoint(BaseModel):