fix: Pass model parameter as config name to NeMo Customizer (#2218)

# What does this PR do?
When launching a fine-tuning job, an upcoming version of NeMo Customizer
will expect the `config` name to be formatted as
`namespace/name@version`. Here, `config` is a reference to a model +
additional metadata. There could be multiple `config`s that reference
the same base model.

This PR updates NVIDIA's `supervised_fine_tune` to simply pass the
`model` param as-is to NeMo Customizer. Currently, it expects a
specific, allowlisted llama model (i.e. `meta/Llama3.1-8B-Instruct`) and
converts it to the provider format (`meta/llama-3.1-8b-instruct`).

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
From a notebook, I built an image with my changes: 
```
!llama stack build --template nvidia --image-type venv
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient

client = LlamaStackAsLibraryClient("nvidia")
client.initialize()
```
And could successfully launch a job:
```
response = client.post_training.supervised_fine_tune(
    job_uuid="",
    model="meta/llama-3.2-1b-instruct@v1.0.0+A100", # Model passed as-is to Customimzer
    ...
)

job_id = response.job_uuid
print(f"Created job with ID: {job_id}")

Output:
Created job with ID: cust-Jm4oGmbwcvoufaLU4XkrRU
```

[//]: # (## Documentation)

---------

Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>

llama_stack/providers/remote/post_training/nvidia/post_training.py

@@ -224,7 +224,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
         Parameters:
             training_config: TrainingConfig - Configuration for training
-            model: str - Model identifier
+            model: str - NeMo Customizer configuration name
             algorithm_config: Optional[AlgorithmConfig] - Algorithm-specific configuration
             checkpoint_dir: Optional[str] - Directory containing model checkpoints, ignored atm
             job_uuid: str - Unique identifier for the job, ignored atm
@@ -299,9 +299,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
             User is informed about unsupported parameters via warnings.
         """
-        # Map model to nvidia model name
-        # See `_MODEL_ENTRIES` for supported models
-        nvidia_model = self.get_provider_model_id(model)
 
         # Check for unsupported method parameters
         unsupported_method_params = []
@@ -347,7 +344,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
 
         # Prepare base job configuration
         job_config = {
-            "config": nvidia_model,
+            "config": model,
             "dataset": {
                 "name": training_config["data_config"]["dataset_id"],
                 "namespace": self.config.dataset_namespace,

tests/unit/providers/nvidia/test_parameters.py

@@ -131,7 +131,7 @@ class TestNvidiaParameters(unittest.TestCase):
 
     def test_required_parameters_passed(self):
         """Test scenario 2: When required parameters are passed."""
-        required_model = "meta-llama/Llama-3.1-8B-Instruct"
+        required_model = "meta/llama-3.2-1b-instruct@v1.0.0+L40"
         required_dataset_id = "required-dataset"
         required_job_uuid = "required-job"
 
@@ -190,7 +190,7 @@ class TestNvidiaParameters(unittest.TestCase):
         self.mock_make_request.assert_called_once()
         call_args = self.mock_make_request.call_args
 
-        assert call_args[1]["json"]["config"] == "meta/llama-3.1-8b-instruct"
+        assert call_args[1]["json"]["config"] == required_model
         assert call_args[1]["json"]["dataset"]["name"] == required_dataset_id
 
     def test_unsupported_parameters_warning(self):

tests/unit/providers/nvidia/test_supervised_fine_tuning.py

@@ -165,7 +165,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             training_job = self.run_async(
                 self.adapter.supervised_fine_tune(
                     job_uuid="1234",
-                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
                     checkpoint_dir="",
                     algorithm_config=algorithm_config,
                     training_config=convert_pydantic_to_json_value(training_config),
@@ -184,7 +184,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             "POST",
             "/v1/customization/jobs",
             expected_json={
-                "config": "meta/llama-3.1-8b-instruct",
+                "config": "meta/llama-3.2-1b-instruct@v1.0.0+L40",
                 "dataset": {"name": "sample-basic-test", "namespace": "default"},
                 "hyperparameters": {
                     "training_type": "sft",
@@ -219,7 +219,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
             self.run_async(
                 self.adapter.supervised_fine_tune(
                     job_uuid="1234",
-                    model="meta-llama/Llama-3.1-8B-Instruct",
+                    model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
                     checkpoint_dir="",
                     algorithm_config=algorithm_config,
                     training_config=convert_pydantic_to_json_value(training_config),