Added vLLM model type to docs

docs/api-reference/model.mdx

@@ -162,3 +162,44 @@ results = experiment.run(
 best_result = experiment.experiment_result.best_run_result
 ```
 </Accordion>
+
+<Accordion title="VLLMModel">
+
+### Overview
+
+The `VLLMModel` class is a subclass of `Model` that is implemented to work with models served through vLLM. It handles the setup and execution, allowing you to easily integrate vLLM-served models them with your applications.
+
+### Class Variables
+
+| Parameter            | Type                       | Default                        | Description                                                                                  | Required |
+|----------------------|----------------------------|--------------------------------|----------------------------------------------------------------------------------------------|----------|
+| `api_url`            | `Optional[str]`            | N/A                            | URL of model being served through vLLM.                                                                       | No       |
+| `required_api_keys`  | `ClassVar[Set[str]]`       | `{}`                           | The set of expected API keys for model being served through vLLM.                                                     | No       |
+| `hyperparameters`    | `ClassVar[Dict]`           | A default hyperparameter search space with `temperature`, `max_tokens`, `top_p`. | A dictionary defining the hyperparameters that can be tuned for the model being served on vLLM.            | No       |
+
+
+### Example Usage
+
+```python
+from nomadic.model import VLLMModel
+from nomadic.tuner import tune
+
+experiment = Experiment(
+    model=VLLMModel(api_url="http://localhost:8000/generate"),
+    params={"temperature", "max_tokens"},
+    evaluation_dataset=json.loads(
+        requests.get(
+            "https://example.com/evaluation_dataset.json"
+        ).content
+    ),
+    evaluator=SemanticSimilarityEvaluator(embed_model=OpenAIEmbedding()),
+)
+
+results = experiment.run(
+    param_dict={
+        "temperature": tune.choice([0.1, 0.5, 0.9]),
+        "max_tokens": tune.choice([50, 100, 200]),
+    })
+best_result = experiment.experiment_result.best_run_result
+```
+</Accordion>