Merge pull request #232 from unslothai/fix/disable-eval-by-default

# fix/disable eval by default

studio/backend/core/training/trainer.py

@@ -343,7 +343,8 @@ class UnslothTrainer:
                      custom_format_mapping: dict = None,
                      subset: str = None,
                      train_split: str = "train",
-                     eval_split: str = None) -> Optional[tuple]:
+                     eval_split: str = None,
+                     eval_steps: float = 0.00) -> Optional[tuple]:
         """
         Load and prepare dataset for training.
 
@@ -358,6 +359,7 @@ class UnslothTrainer:
             dataset = None
             eval_dataset = None
             has_separate_eval_source = False  # True if eval comes from a separate HF split
+            eval_enabled = eval_steps is not None and eval_steps > 0
 
             if local_datasets:
                 # Load local datasets
@@ -410,23 +412,26 @@ class UnslothTrainer:
                 print(f"Loaded dataset from Hugging Face: {dataset_source}\n")
 
                 # Resolve eval split from a separate HF split (explicit or auto-detected)
-                if eval_split:
-                    # Explicit eval split provided - load it directly
-                    print(f"Loading explicit eval split: '{eval_split}'\n")
-                    eval_load_kwargs = {"path": dataset_source, "split": eval_split}
-                    if subset:
-                        eval_load_kwargs["name"] = subset
-                    eval_dataset = load_dataset(**eval_load_kwargs)
-                    has_separate_eval_source = True
-                    print(f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n")
-                else:
-                    # Auto-detect eval split from HF (returns a separate dataset, or None)
-                    eval_dataset = self._auto_detect_eval_split_from_hf(
-                        dataset_source=dataset_source,
-                        subset=subset,
-                    )
-                    if eval_dataset is not None:
+                if eval_enabled:
+                    if eval_split:
+                        # Explicit eval split provided - load it directly
+                        print(f"Loading explicit eval split: '{eval_split}'\n")
+                        eval_load_kwargs = {"path": dataset_source, "split": eval_split}
+                        if subset:
+                            eval_load_kwargs["name"] = subset
+                        eval_dataset = load_dataset(**eval_load_kwargs)
                         has_separate_eval_source = True
+                        print(f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n")
+                    else:
+                        # Auto-detect eval split from HF (returns a separate dataset, or None)
+                        eval_dataset = self._auto_detect_eval_split_from_hf(
+                            dataset_source=dataset_source,
+                            subset=subset,
+                        )
+                        if eval_dataset is not None:
+                            has_separate_eval_source = True
+                else:
+                    print("Eval disabled (eval_steps <= 0), skipping eval split detection\n")
 
             if dataset is None:
                 raise ValueError("No dataset provided")
@@ -472,7 +477,7 @@ class UnslothTrainer:
                 )
                 eval_dataset = eval_info["dataset"]
                 print(f"Eval dataset formatted successfully\n")
-            elif not has_separate_eval_source:
+            elif eval_enabled and not has_separate_eval_source:
                 # No separate eval source — split the already-formatted dataset
                 formatted_dataset = dataset_info["dataset"]
                 split_result = self._resolve_eval_split_from_dataset(formatted_dataset)
@@ -543,7 +548,7 @@ class UnslothTrainer:
     def start_training(self,
                        dataset: Dataset,
                        eval_dataset: Dataset = None,
-                       eval_steps: float = 0.01,
+                       eval_steps: float = 0.00,
                        output_dir: str = "./outputs",
                        num_epochs: int = 3,
                        learning_rate: float = 5e-5,
@@ -743,12 +748,16 @@ class UnslothTrainer:
 
             # ========== EVAL CONFIGURATION ==========
             eval_dataset = training_args.get('eval_dataset', None)
-            eval_steps_val = training_args.get('eval_steps', 0.01)
+            eval_steps_val = training_args.get('eval_steps', 0.00)
             if eval_dataset is not None:
-                config_args["eval_strategy"] = "steps"
-                config_args["eval_steps"] = eval_steps_val
-                print(f"Evaluation enabled: eval_steps={eval_steps_val} (fraction of total steps)\n")
-                print(f"Eval dataset: {len(eval_dataset)} rows\n")
+                if eval_steps_val > 0:
+                    config_args["eval_strategy"] = "steps"
+                    config_args["eval_steps"] = eval_steps_val
+                    print(f"✅ Evaluation enabled: eval_steps={eval_steps_val} (fraction of total steps)\n")
+                    print(f"Eval dataset: {len(eval_dataset)} rows\n")
+                else:
+                    print(f"⚠️  Eval dataset provided but eval_steps={eval_steps_val} (disabled)\n")
+                    print("To enable evaluation, set eval_steps > 0.0\n")
             else:
                 print("No eval dataset — evaluation disabled\n")
 

studio/backend/core/training/training.py

@@ -115,7 +115,7 @@ class TrainingBackend:
                        subset: str = None,
                        train_split: str = "train",
                        eval_split: str = None,
-                       eval_steps: float = 0.01,
+                       eval_steps: float = 0.00,
                        is_dataset_multimodal: bool = False) -> bool:
         """
         Start training.
@@ -223,6 +223,7 @@ class TrainingBackend:
                 subset=subset,
                 train_split=train_split,
                 eval_split=eval_split,
+                eval_steps=eval_steps,
             )
 
             # Unpack: load_and_format_dataset returns (dataset, eval_dataset)
@@ -232,10 +233,6 @@ class TrainingBackend:
                 dataset = dataset_result
                 eval_dataset = None
 
-            # If user set eval_steps to 0, disable evaluation entirely
-            if eval_steps is not None and float(eval_steps) <= 0:
-                eval_dataset = None
-
             # Track whether eval is enabled for status reporting
             self.eval_enabled = eval_dataset is not None
 

studio/backend/models/training.py

@@ -21,7 +21,7 @@ class TrainingStartRequest(BaseModel):
     subset: Optional[str] = None
     train_split: Optional[str] = Field("train", description="Training split name")
     eval_split: Optional[str] = Field(None, description="Eval split name. None = auto-detect")
-    eval_steps: float = Field(0.01, description="Fraction of total steps between evals (0-1)")
+    eval_steps: float = Field(0.00, description="Fraction of total steps between evals (0-1)")
 
     @model_validator(mode="before")
     @classmethod

studio/frontend/src/config/training.ts

@@ -103,7 +103,7 @@ export const DEFAULT_HYPERPARAMS = {
   warmupSteps: 5,
   maxSteps: 0,
   saveSteps: 0,
-  evalSteps: 0.01,
+  evalSteps: 0.00,
   packing: false,
   trainOnCompletions: false,
   gradientCheckpointing: "unsloth" as const,