Sambanova - LlamaGuard (#886)

# What does this PR do?

- Fix loading SambaNovaImpl issue
- Add LlamaGuard model support for inference

## Test Plan

Run the following unit test scripts and results

### Embedding
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_embeddings.py --inference-model meta-llama/Llama-3.2-11B-Vision-Instruct --env SAMBANOVA_API_KEY={SAMBANOVA_API_KEY}
```
```
llama_stack/providers/tests/inference/test_embeddings.py::TestEmbeddings::test_embeddings[-sambanova] SKIPPED (This test is only applicable for embedding models)
llama_stack/providers/tests/inference/test_embeddings.py::TestEmbeddings::test_batch_embeddings[-sambanova] SKIPPED (This test is only applicable for embedding models)

=================================================================================================================== 2 skipped, 1 warning in 0.32s ===================================================================================================================
```

### Vision
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_vision_inference.py --inference-model meta-llama/Llama-3.2-11B-Vision-Instruct --env SAMBANOVA_API_KEY={SAMBANOVA_API_KEY}
```

```
llama_stack/providers/tests/inference/test_vision_inference.py::TestVisionModelInference::test_vision_chat_completion_non_streaming[-sambanova-image0-expected_strings0] PASSED
llama_stack/providers/tests/inference/test_vision_inference.py::TestVisionModelInference::test_vision_chat_completion_non_streaming[-sambanova-image1-expected_strings1] PASSED
llama_stack/providers/tests/inference/test_vision_inference.py::TestVisionModelInference::test_vision_chat_completion_streaming[-sambanova] PASSED

=================================================================================================================== 3 passed, 1 warning in 2.68s ====================================================================================================================
```

### Text
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming --env SAMBANOVA_API_KEY={SAMBANOVA_API_KEY}
```

```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[-sambanova] PASSED

=================================================================================================================== 1 passed, 1 warning in 0.46s ====================================================================================================================
```

```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming --env SAMBANOVA_API_KEY={SAMBANOVA_API_KEY}
```

```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[-sambanova] PASSED

=================================================================================================================== 1 passed, 1 warning in 0.48s ====================================================================================================================
```




## Before submitting

- [] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [Y] Ran pre-commit to handle lint / formatting issues.
- [Y] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [Y] Updated relevant documentation.
- [Y] Wrote necessary unit or integration tests.

llama_stack/providers/remote/inference/sambanova/config.py

@@ -22,7 +22,7 @@ class SambaNovaImplConfig(BaseModel):
     )
 
     @classmethod
-    def sample_run_config(cls) -> Dict[str, Any]:
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
         return {
             "url": "https://api.sambanova.ai/v1",
             "api_key": "${env.SAMBANOVA_API_KEY}",

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -7,7 +7,12 @@
 import json
 from typing import AsyncGenerator
 
-from llama_models.datatypes import CoreModelId, SamplingStrategy
+from llama_models.datatypes import (
+    CoreModelId,
+    GreedySamplingStrategy,
+    TopKSamplingStrategy,
+    TopPSamplingStrategy,
+)
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
 from openai import OpenAI
@@ -60,6 +65,10 @@ MODEL_ALIASES = [
         "Llama-3.2-90B-Vision-Instruct",
         CoreModelId.llama3_2_90b_vision_instruct.value,
     ),
+    build_model_alias(
+        "Meta-Llama-Guard-3-8B",
+        CoreModelId.llama_guard_3_8b.value,
+    ),
 ]
 
 
@@ -197,12 +206,12 @@ class SambaNovaInferenceAdapter(ModelRegistryHelper, Inference):
                 else:
                     params["max_completion_tokens"] = sampling_params.max_tokens
 
-            if sampling_params.strategy == SamplingStrategy.top_p:
-                params["top_p"] = sampling_params.top_p
-            elif sampling_params.strategy == "top_k":
-                params["extra_body"]["top_k"] = sampling_params.top_k
-            elif sampling_params.strategy == "greedy":
-                params["temperature"] = sampling_params.temperature
+            if isinstance(sampling_params.strategy, TopPSamplingStrategy):
+                params["top_p"] = sampling_params.strategy.top_p
+            if isinstance(sampling_params.strategy, TopKSamplingStrategy):
+                params["extra_body"]["top_k"] = sampling_params.strategy.top_k
+            if isinstance(sampling_params.strategy, GreedySamplingStrategy):
+                params["temperature"] = 0.0
 
         return params