support llama3.1 8B instruct in post training (#698)

## What does this PR do? 
- Change to support llama3.1 8B instruct model other than llama3 8B
model as llama3.1 8B instruct model is a better model to finetune on top
of
- Make the copy files logic in checkpointer safer in case the file be
copied doesn't exist in source path

## test
issue a post training request from client and verify training works as
expect
<img width="1101" alt="Screenshot 2025-01-02 at 12 18 45 PM"
src="https://github.com/user-attachments/assets/47cc4df9-3edc-4afd-b5dd-abe1f039f1ed"
/>

<img width="782" alt="Screenshot 2025-01-02 at 12 18 52 PM"
src="https://github.com/user-attachments/assets/b9435274-ef1d-4570-bd8e-0880c3a4b2e9"
/>

llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py

@@ -90,18 +90,24 @@ class TorchtuneCheckpointer:
         model_file_path.mkdir(parents=True, exist_ok=True)
 
         # copy the related files for inference
-        shutil.copy(
-            Path.joinpath(self._checkpoint_dir, "params.json"),
-            Path.joinpath(model_file_path, "params.json"),
-        )
-        shutil.copy(
-            Path.joinpath(self._checkpoint_dir, "tokenizer.model"),
-            Path.joinpath(model_file_path, "tokenizer.model"),
-        )
-        shutil.copy(
-            Path.joinpath(self._checkpoint_dir, "orig_params.json"),
-            Path.joinpath(model_file_path, "orig_params.json"),
-        )
+        source_path = Path.joinpath(self._checkpoint_dir, "params.json")
+        if source_path.exists():
+            shutil.copy(
+                source_path,
+                Path.joinpath(model_file_path, "params.json"),
+            )
+        source_path = Path.joinpath(self._checkpoint_dir, "tokenizer.model")
+        if source_path.exists():
+            shutil.copy(
+                source_path,
+                Path.joinpath(model_file_path, "tokenizer.model"),
+            )
+        source_path = Path.joinpath(self._checkpoint_dir, "orig_params.json")
+        if source_path.exists():
+            shutil.copy(
+                source_path,
+                Path.joinpath(model_file_path, "orig_params.json"),
+            )
 
         if not adapter_only:
             model_state_dict = state_dict[training.MODEL_KEY]

llama_stack/providers/inline/post_training/torchtune/common/utils.py

@@ -21,8 +21,9 @@ from llama_stack.apis.datasets import Datasets
 
 from pydantic import BaseModel
 
-from torchtune.models.llama3 import llama3_tokenizer, lora_llama3_8b
+from torchtune.models.llama3 import llama3_tokenizer
 from torchtune.models.llama3._tokenizer import Llama3Tokenizer
+from torchtune.models.llama3_1 import lora_llama3_1_8b
 from torchtune.models.llama3_2 import lora_llama3_2_3b
 
 
@@ -49,8 +50,8 @@ MODEL_CONFIGS: Dict[str, ModelConfig] = {
         tokenizer_type=llama3_tokenizer,
         checkpoint_type="LLAMA3_2",
     ),
-    "Llama-3-8B-Instruct": ModelConfig(
-        model_definition=lora_llama3_8b,
+    "Llama3.1-8B-Instruct": ModelConfig(
+        model_definition=lora_llama3_1_8b,
         tokenizer_type=llama3_tokenizer,
         checkpoint_type="LLAMA3",
     ),