diff --git a/llama_stack/providers/inline/post_training/torchtune/utils.py b/llama_stack/providers/inline/post_training/torchtune/utils.py
index 56a3c2fa2..6f7c18f72 100644
--- a/llama_stack/providers/inline/post_training/torchtune/utils.py
+++ b/llama_stack/providers/inline/post_training/torchtune/utils.py
@@ -16,6 +16,7 @@ from typing import Any, Callable, Dict, List
 import torch
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.common.type_system import *  # noqa
+from llama_models.datatypes import Model
 from llama_models.sku_list import resolve_model
 from llama_stack.apis.common.type_system import ParamType
 
@@ -31,18 +32,29 @@ class ColumnName(Enum):
     text = "text"
 
 
-MODEL_CONFIGS: Dict[str, Dict[str, Any]] = {
-    "Llama3.2-3B-Instruct": {
-        "model_definition": lora_llama3_2_3b,
-        "tokenizer_type": llama3_tokenizer,
-        "checkpoint_type": "LLAMA3_2",
-    },
-    "Llama-3-8B-Instruct": {
-        "model_definition": lora_llama3_8b,
-        "tokenizer_type": llama3_tokenizer,
-        "checkpoint_type": "LLAMA3",
-    },
-}
+class ModelConfig(BaseModel):
+    model_definition: Any
+    tokenizer_type: Any
+    checkpoint_type: str
+
+
+class ModelConfigs(BaseModel):
+    Llama3_2_3B_Instruct: ModelConfig
+    Llama_3_8B_Instruct: ModelConfig
+
+
+MODEL_CONFIGS = ModelConfigs(
+    Llama3_2_3B_Instruct=ModelConfig(
+        model_definition=lora_llama3_2_3b,
+        tokenizer_type=llama3_tokenizer,
+        checkpoint_type="LLAMA3_2",
+    ),
+    Llama_3_8B_Instruct=ModelConfig(
+        model_definition=lora_llama3_8b,
+        tokenizer_type=llama3_tokenizer,
+        checkpoint_type="LLAMA3",
+    ),
+)
 
 EXPECTED_DATASET_SCHEMA: Dict[str, List[Dict[str, ParamType]]] = {
     "alpaca": [
@@ -68,20 +80,38 @@ BuildLoraModelCallable = Callable[..., torch.nn.Module]
 BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
 
 
+def _modify_model_id(model_id: str) -> str:
+    return model_id.replace("-", "_").replace(".", "_")
+
+
+def _validate_model_id(model_id: str) -> Model:
+    model = resolve_model(model_id)
+    modified_model_id = _modify_model_id(model.core_model_id.value)
+    if model is None or not hasattr(MODEL_CONFIGS, modified_model_id):
+        raise ValueError(f"Model {model_id} is not supported.")
+    return model
+
+
 async def get_model_definition(
     model_id: str,
 ) -> BuildLoraModelCallable:
-    model = resolve_model(model_id)
-    if model is None or model.core_model_id.value not in MODEL_CONFIGS:
-        raise ValueError(f"Model {model_id} is not supported.")
-    return MODEL_CONFIGS[model.core_model_id.value]["model_definition"]
+    model = _validate_model_id(model_id)
+    modified_model_id = _modify_model_id(model.core_model_id.value)
+    model_config = getattr(MODEL_CONFIGS, modified_model_id)
+    if not hasattr(model_config, "model_definition"):
+        raise ValueError(f"Model {model_id} does not have model definition.")
+    return model_config.model_definition
 
 
 async def get_tokenizer_type(
     model_id: str,
 ) -> BuildTokenizerCallable:
-    model = resolve_model(model_id)
-    return MODEL_CONFIGS[model.core_model_id.value]["tokenizer_type"]
+    model = _validate_model_id(model_id)
+    modified_model_id = _modify_model_id(model.core_model_id.value)
+    model_config = getattr(MODEL_CONFIGS, modified_model_id)
+    if not hasattr(model_config, "tokenizer_type"):
+        raise ValueError(f"Model {model_id} does not have tokenizer_type.")
+    return model_config.tokenizer_type
 
 
 async def get_checkpointer_model_type(
@@ -91,8 +121,12 @@ async def get_checkpointer_model_type(
     checkpointer model type is used in checkpointer for some special treatment on some specific model types
     For example, llama3.2 model tied weights (https://github.com/pytorch/torchtune/blob/main/torchtune/training/checkpointing/_checkpointer.py#L1041)
     """
-    model = resolve_model(model_id)
-    return MODEL_CONFIGS[model.core_model_id.value]["checkpoint_type"]
+    model = _validate_model_id(model_id)
+    modified_model_id = _modify_model_id(model.core_model_id.value)
+    model_config = getattr(MODEL_CONFIGS, modified_model_id)
+    if not hasattr(model_config, "checkpoint_type"):
+        raise ValueError(f"Model {model_id} does not have checkpoint_type.")
+    return model_config.checkpoint_type
 
 
 async def validate_input_dataset_schema(