set eval_strategy from config

src/axolotl/utils/trainer.py

@@ -556,20 +556,24 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             "sample_packing_efficiency"
         ] = cfg.sample_packing_eff_est
 
-    if cfg.val_set_size == 0:
+    if cfg.eval_steps and cfg.evaluation_strategy:
+        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
+        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
+    elif cfg.val_set_size == 0:
         training_arguments_kwargs["evaluation_strategy"] = "no"
-    elif cfg.eval_steps:
+    elif cfg.eval_steps and isinstance(cfg.eval_steps, int):
         training_arguments_kwargs["evaluation_strategy"] = "steps"
         training_arguments_kwargs["eval_steps"] = cfg.eval_steps
     else:
-        # we have an eval set, but no steps defined, use epoch
         training_arguments_kwargs["evaluation_strategy"] = "epoch"
+        if cfg.eval_steps and isinstance(cfg.eval_steps, float):
+            training_arguments_kwargs["eval_steps"] = cfg.eval_steps
 
     if cfg.save_strategy:
         training_arguments_kwargs["save_strategy"] = cfg.save_strategy
     else:
         training_arguments_kwargs["save_strategy"] = (
-            "steps" if cfg.save_steps else "epoch"
+            "steps" if cfg.save_steps and isinstance(cfg.save_steps, int) else "epoch"
         )
 
     if cfg.do_bench_eval: