feat: NVIDIA allow non-llama model registration (#1859)

# What does this PR do?
Adds custom model registration functionality to NVIDIAInferenceAdapter
which let's the inference happen on:
- post-training model
- non-llama models in API Catalogue(behind
https://integrate.api.nvidia.com and endpoints compatible with
AyncOpenAI)

## Example Usage:
```python
from llama_stack.apis.models import Model, ModelType
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
_ = client.initialize()

client.models.register(
        model_id=model_name,
        model_type=ModelType.llm,
        provider_id="nvidia"
)

response = client.inference.chat_completion(
    model_id=model_name,
    messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Write a limerick about the wonders of GPU computing."}],
)
```

## Test Plan
```bash
pytest tests/unit/providers/nvidia/test_supervised_fine_tuning.py 
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0
collected 6 items                                                                                                                        

tests/unit/providers/nvidia/test_supervised_fine_tuning.py ......                                                                  [100%]

============================================================ warnings summary ============================================================
../miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076
  /home/ubuntu/miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'contentEncoding'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/
    warn(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 6 passed, 1 warning in 1.51s ======================================================
```

[//]: # (## Documentation)
Updated Readme.md

cc: @dglogo, @sumitb, @mattf

tests/unit/providers/nvidia/test_supervised_fine_tuning.py

@@ -17,6 +17,8 @@ from llama_stack_client.types.post_training_supervised_fine_tune_params import (
     TrainingConfigOptimizerConfig,
 )
 
+from llama_stack.apis.models import Model, ModelType
+from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter
 from llama_stack.providers.remote.post_training.nvidia.post_training import (
     ListNvidiaPostTrainingJobs,
     NvidiaPostTrainingAdapter,
@@ -40,8 +42,22 @@ class TestNvidiaPostTraining(unittest.TestCase):
         )
         self.mock_make_request = self.make_request_patcher.start()
 
+        # Mock the inference client
+        inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None)
+        self.inference_adapter = NVIDIAInferenceAdapter(inference_config)
+
+        self.mock_client = unittest.mock.MagicMock()
+        self.mock_client.chat.completions.create = unittest.mock.AsyncMock()
+        self.inference_mock_make_request = self.mock_client.chat.completions.create
+        self.inference_make_request_patcher = patch(
+            "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client",
+            return_value=self.mock_client,
+        )
+        self.inference_make_request_patcher.start()
+
     def tearDown(self):
         self.make_request_patcher.stop()
+        self.inference_make_request_patcher.stop()
 
     @pytest.fixture(autouse=True)
     def inject_fixtures(self, run_async):
@@ -303,6 +319,31 @@ class TestNvidiaPostTraining(unittest.TestCase):
             expected_params={"job_id": job_id},
         )
 
+    def test_inference_register_model(self):
+        model_id = "default/job-1234"
+        model_type = ModelType.llm
+        model = Model(
+            identifier=model_id,
+            provider_id="nvidia",
+            provider_model_id=model_id,
+            provider_resource_id=model_id,
+            model_type=model_type,
+        )
+        result = self.run_async(self.inference_adapter.register_model(model))
+        assert result == model
+        assert len(self.inference_adapter.alias_to_provider_id_map) > 1
+        assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id
+
+        with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion:
+            self.run_async(
+                self.inference_adapter.chat_completion(
+                    model_id=model_id,
+                    messages=[{"role": "user", "content": "Hello, model"}],
+                )
+            )
+
+            mock_chat_completion.assert_called()
+
 
 if __name__ == "__main__":
     unittest.main()