diff --git a/src/axolotl/utils/trainer.py b/src/axolotl/utils/trainer.py
index 54ad058f5..eaf50d2d9 100644
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -452,13 +452,13 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         ] = cfg.sample_packing_eff_est
 
     if cfg.val_set_size == 0:
-        evaluation_strategy = "no"
-    elif cfg.eval_steps < 1:
-        # eval every epoch
-        evaluation_strategy = "epoch"
+        training_arguments_kwargs["evaluation_strategy"] = "no"
+    elif cfg.eval_steps:
+        training_arguments_kwargs["evaluation_strategy"] = "steps"
+        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
     else:
-        # eval every eval_steps steps
-        evaluation_strategy = "steps"
+        # we have an eval set, but no steps defined, use epoch
+        training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
     training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
         max_steps=total_num_steps if cfg.max_steps else -1,
@@ -471,9 +471,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,
         learning_rate=cfg.learning_rate,
-        evaluation_strategy=evaluation_strategy,
         save_strategy="steps" if cfg.save_steps else "epoch",
-        eval_steps=cfg.eval_steps if cfg.val_set_size > 0 else None,
         save_steps=cfg.save_steps,
         output_dir=cfg.output_dir,
         save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4,