diff --git a/fast_llm/engine/checkpoint/external.py b/fast_llm/engine/checkpoint/external.py
index fea599c5..529697c9 100644
--- a/fast_llm/engine/checkpoint/external.py
+++ b/fast_llm/engine/checkpoint/external.py
@@ -238,10 +238,12 @@ def _export_config(cls, config: BaseModelArchitectureConfig) -> dict[str, typing
         exported_config = {}
         for converter in cls._get_config_converters():
             try:
-                values = [
-                    converter.export_params(cls._get_fast_llm_attribute(config, fast_llm_name))
-                    for fast_llm_name in converter.fast_llm_names
-                ]
+                values = converter.export_params(
+                    tuple(
+                        cls._get_fast_llm_attribute(config, fast_llm_name)
+                        for fast_llm_name in converter.fast_llm_names
+                    )
+                )
                 for export_name, value in zip(converter.export_names, values, strict=True):
                     if value is not MISSING:
                         set_nested_dict_value(exported_config, export_name, value)
diff --git a/fast_llm/models/gpt/conversion.py b/fast_llm/models/gpt/conversion.py
index 55f595a6..13f9f938 100644
--- a/fast_llm/models/gpt/conversion.py
+++ b/fast_llm/models/gpt/conversion.py
@@ -299,9 +299,9 @@ def __post_init__(self):
     def export_params(self, fast_llm_values):
         rope_type, *parameters = fast_llm_values
         if rope_type == RotaryEmbeddingType.default:
-            return None
+            return (None,)
         elif rope_type == RotaryEmbeddingType.llama3:
-            return {key: value for key, value in zip(self._HUGGINGFACE_NAMES, ("llama3",) + parameters, strict=True)}
+            return ({key: value for key, value in zip(self._HUGGINGFACE_NAMES, ("llama3", *parameters), strict=True)},)
         else:
             raise ValueError(f"Unsupported rotary scaling type: {rope_type}")
 
@@ -311,7 +311,7 @@ def import_params(self, export_values):
             return (RotaryEmbeddingType.default,) + (DEFAULT,) * 4
         elif rope_type == RotaryEmbeddingType.llama3:
             # TODO: Is it safe to assume all values are provided?
-            return {self._HUGGINGFACE_NAMES[0]: "llama3", **{export_value[key] for key in self._HUGGINGFACE_NAMES[1:]}}
+            return ("llama3", *[export_value[key] for key in self._HUGGINGFACE_NAMES[1:]])
         else:
             raise ValueError(f"Unsupported rotary scaling type: {rope_type}")
 
diff --git a/tests/common.py b/tests/common.py
index b96a2204..139e0838 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -30,6 +30,7 @@
 FORCE_REUSE_RESULTS = int(os.environ.get("FORCE_REUSE_RESULTS", 0)) != 0
 REUSE_RESULTS = FORCE_REUSE_RESULTS or int(os.environ.get("REUSE_RESULTS", 0)) != 0
 _LOG_LEVEL = int(os.environ.get("LOG_LEVEL", 13))
+TEST_MODEL = os.environ.get("MODEL", "llama")
 
 ARTIFACT_PATH = "runs/0/artifacts"
 
@@ -111,14 +112,14 @@
 ]
 CONFIG_SC2_COMMON = CONFIG_SC2_FAST_LLM + ["model.distributed.training_dtype=bf16"]
 
-CONFIG_MISTRAL_MEGATRON = CONFIG_SC2_MEGATRON + [
+CONFIG_LLAMA_MEGATRON = CONFIG_SC2_MEGATRON + [
     "--swiglu",
     "--disable-bias-linear",
     "--normalization=RMSNorm",
     "--ffn-hidden-size=1024",
     "--untie-embeddings-and-output-weights",
 ]
-CONFIG_MISTRAL_FAST_LLM = CONFIG_SC2_FAST_LLM + [
+CONFIG_LLAMA_FAST_LLM = CONFIG_SC2_FAST_LLM + [
     "model.base_model.transformer.gated=True",
     "model.base_model.transformer.activation_type=silu",
     "model.base_model.transformer.add_linear_biases=False",
@@ -126,29 +127,25 @@
     "model.base_model.transformer.ffn_hidden_size=1024",
     "model.base_model.tie_word_embeddings=False",
 ]
-CONFIG_MISTRAL_COMMON = CONFIG_MISTRAL_FAST_LLM + ["model.distributed.training_dtype=bf16"]
+CONFIG_LLAMA_COMMON = CONFIG_LLAMA_FAST_LLM + ["model.distributed.training_dtype=bf16"]
 
-CONFIG_MIXTRAL_MEGATRON = CONFIG_MISTRAL_MEGATRON + [
+# Megatron does not support Llama3-style Rotary Embeddings
+CONFIG_LLAMA3_MEGATRON = None
+CONFIG_LLAMA3_FAST_LLM = CONFIG_LLAMA_FAST_LLM + [
+    "model.base_model.transformer.rotary.type=llama3",
+]
+CONFIG_LLAMA3_COMMON = CONFIG_LLAMA3_FAST_LLM + ["model.distributed.training_dtype=bf16"]
+
+CONFIG_MIXTRAL_MEGATRON = CONFIG_LLAMA_MEGATRON + [
     "--num-experts=4",
     "--moe-router-topk=4",
 ]
-CONFIG_MIXTRAL_FAST_LLM = CONFIG_MISTRAL_FAST_LLM + [
+CONFIG_MIXTRAL_FAST_LLM = CONFIG_LLAMA_FAST_LLM + [
     "model.base_model.transformer.num_experts=4",
     "model.base_model.transformer.num_experts_per_token=4",
 ]
 CONFIG_MIXTRAL_COMMON = CONFIG_MIXTRAL_FAST_LLM + ["model.distributed.training_dtype=bf16"]
 
-CONFIG_LLAMA3_MEGATRON = None  # Megatron does not support Llama3-style Rotary Embeddings
-CONFIG_LLAMA3_FAST_LLM = CONFIG_SC2_FAST_LLM + [
-    "model.base_model.transformer.gated=True",
-    "model.base_model.transformer.activation_type=silu",
-    "model.base_model.transformer.add_linear_biases=False",
-    "model.base_model.transformer.normalization.type=rms_norm",
-    "model.base_model.transformer.rotary.type=llama3",
-    "model.base_model.tie_word_embeddings=False",
-]
-CONFIG_LLAMA3_COMMON = CONFIG_LLAMA3_FAST_LLM + ["model.distributed.training_dtype=bf16"]
-
 _CONFIGS = {
     "gpt2": ("gpt", CONFIG_GPT2_FAST_LLM, CONFIG_GPT2_MEGATRON, CONFIG_GPT2_COMMON, None),
     "sc1": ("gpt", HuggingfaceGPTModelForCausalLM, CONFIG_SC1_FAST_LLM, CONFIG_SC1_MEGATRON, CONFIG_SC1_COMMON, None),
@@ -159,11 +156,25 @@
         CONFIG_SC2_COMMON,
         Starcoder2GPTHuggingfaceCheckpointFormat,
     ),
+    "llama": (
+        "gpt",
+        CONFIG_LLAMA_FAST_LLM,
+        CONFIG_LLAMA_MEGATRON,
+        CONFIG_LLAMA_COMMON,
+        LlamaGPTHuggingfaceCheckpointFormat,
+    ),
+    "llama3": (
+        "gpt",
+        CONFIG_LLAMA3_FAST_LLM,
+        CONFIG_LLAMA3_MEGATRON,
+        CONFIG_LLAMA3_COMMON,
+        LlamaGPTHuggingfaceCheckpointFormat,
+    ),
     "mistral": (
         "gpt",
-        CONFIG_MISTRAL_FAST_LLM,
-        CONFIG_MISTRAL_MEGATRON,
-        CONFIG_MISTRAL_COMMON,
+        CONFIG_LLAMA_FAST_LLM,
+        CONFIG_LLAMA_MEGATRON,
+        CONFIG_LLAMA_COMMON,
         MistralGPTHuggingfaceCheckpointFormat,
     ),
     "mixtral": (
@@ -173,18 +184,9 @@
         CONFIG_MIXTRAL_COMMON,
         MixtralGPTHuggingfaceCheckpointFormat,
     ),
-    "llama3": (
-        "gpt",
-        CONFIG_LLAMA3_FAST_LLM,
-        CONFIG_LLAMA3_MEGATRON,
-        CONFIG_LLAMA3_COMMON,
-        LlamaGPTHuggingfaceCheckpointFormat,
-    ),
 }
 
 
-TEST_MODEL = os.environ.get("MODEL", "mistral")
-
 TEST_MODEL_TYPE, CONFIG_FAST_LLM, CONFIG_GPT2, CONFIG_COMMON, HUGGINGFACE_CHECKPOINT_FORMAT = _CONFIGS[TEST_MODEL]
 
 
diff --git a/tests/test_match_megatron.py b/tests/test_match_megatron.py
index 354b5188..37f63b2d 100644
--- a/tests/test_match_megatron.py
+++ b/tests/test_match_megatron.py
@@ -3,8 +3,8 @@
 from tests.common import (
     CONFIG_GPT2_FAST_LLM,
     CONFIG_GPT2_MEGATRON,
-    CONFIG_MISTRAL_FAST_LLM,
-    CONFIG_MISTRAL_MEGATRON,
+    CONFIG_LLAMA_FAST_LLM,
+    CONFIG_LLAMA_MEGATRON,
     CONFIG_MIXTRAL_FAST_LLM,
     CONFIG_MIXTRAL_MEGATRON,
     CONFIG_SC1_FAST_LLM,
@@ -100,7 +100,7 @@ def test_gpt2_match_meg():
 def test_mistral_meg():
     # Mistral with Megatron.
     # No linear bias, swiglu activation, RMSNorm
-    run_test_script("test_mistral_meg", CONFIG_MISTRAL_MEGATRON + ["--micro-batch-size=8"], is_megatron=True)
+    run_test_script("test_mistral_meg", CONFIG_LLAMA_MEGATRON + ["--micro-batch-size=8"], is_megatron=True)
 
 
 @pytest.mark.depends(on=["test_mistral_meg"])
@@ -108,7 +108,7 @@ def test_mistral_match_meg():
     # Mistral with Fast-LLM.
     run_test_script(
         "test_mistral_match_meg",
-        CONFIG_MISTRAL_FAST_LLM + ["model.base_model.use_megatron_initialization=True"],
+        CONFIG_LLAMA_FAST_LLM + ["model.base_model.use_megatron_initialization=True"],
         compare="test_mistral_meg",
         config=CompareConfig(
             ignore_tensors=[