diff --git a/nemo/collections/common/parts/utils.py b/nemo/collections/common/parts/utils.py
index e08f7d7101834..c00de27c55bd0 100644
--- a/nemo/collections/common/parts/utils.py
+++ b/nemo/collections/common/parts/utils.py
@@ -112,14 +112,9 @@ def extend_instance(obj, mixin):
     )  # mixin needs to go first for our forward() logic to work
 
 
-def apply_rope_scaling(freqs):
+def apply_rope_scaling(freqs, scale_factor=8, low_freq_factor=1, high_freq_factor=4, old_context_len=8192):
     # Apply scaling for RoPE frequencies
     logger.info("apply rope scaling ...")
-    # Values obtained from grid search
-    scale_factor = 8
-    low_freq_factor = 1
-    high_freq_factor = 4
-    old_context_len = 8192  # original llama3 length
 
     low_freq_wavelen = old_context_len / low_freq_factor
     high_freq_wavelen = old_context_len / high_freq_factor
diff --git a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
index 165a6d650843c..02ef522dde1f8 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
@@ -199,7 +199,13 @@ def mcore_model_customize(cfg, model):
     if cfg.get("apply_embedding_scaling", False) and parallel_state.is_pipeline_first_stage():
         extend_instance(model.embedding, EmbeddingScalingMixin)
     if cfg.get("scale_positional_embedding", False):
-        model.rotary_pos_emb.inv_freq = apply_rope_scaling(model.rotary_pos_emb.inv_freq)
+        model.rotary_pos_emb.inv_freq = apply_rope_scaling(
+            model.rotary_pos_emb.inv_freq,
+            scale_factor=cfg.get('scale_factor', 8),
+            low_freq_factor=cfg.get('low_freq_factor', 1),
+            high_freq_factor=cfg.get('high_freq_factor', 4),
+            old_context_len=cfg.get('old_context_len', 8192),
+        )
     if cfg.get("mcore_customization_config", {}).get("final_logit_softcapping", 0):
         from nemo.collections.nlp.models.language_modeling.megatron.gemma2.gemma2_modules import Gemma2OutputLayer