Implement TGI model config from path (#448)

Implement TGI model config from path:
```python
TGIModelConfig.from_path(model_config_path)
```

Follow-up to:
- #434 

Related to:
- #439

src/lighteval/main_endpoint.py

@@ -314,7 +314,6 @@ def tgi(
     """
     Evaluate models using TGI as backend.
     """
-    import yaml
 
     from lighteval.logging.evaluation_tracker import EvaluationTracker
     from lighteval.models.endpoints.tgi_model import TGIModelConfig
@@ -332,14 +331,8 @@ def tgi(
 
     # TODO (nathan): better handling of model_args
     parallelism_manager = ParallelismManager.TGI
-    with open(model_config_path, "r") as f:
-        config = yaml.safe_load(f)["model"]
 
-    model_config = TGIModelConfig(
-        inference_server_address=config["instance"]["inference_server_address"],
-        inference_server_auth=config["instance"]["inference_server_auth"],
-        model_id=config["instance"]["model_id"],
-    )
+    model_config = TGIModelConfig.from_path(model_config_path)
 
     pipeline_params = PipelineParameters(
         launcher_type=parallelism_manager,

src/lighteval/models/endpoints/endpoint_model.py

@@ -111,6 +111,14 @@ def __post_init__(self):
 
     @classmethod
     def from_path(cls, path: str) -> "InferenceEndpointModelConfig":
+        """Load configuration for inference endpoint model from YAML file path.
+
+        Args:
+            path (`str`): Path of the model configuration YAML file.
+
+        Returns:
+            [`InferenceEndpointModelConfig`]: Configuration for inference endpoint model.
+        """
         import yaml
 
         with open(path, "r") as f:

src/lighteval/models/endpoints/tgi_model.py

@@ -51,6 +51,22 @@ class TGIModelConfig:
     inference_server_auth: str
     model_id: str
 
+    @classmethod
+    def from_path(cls, path: str) -> "TGIModelConfig":
+        """Load configuration for TGI endpoint model from YAML file path.
+
+        Args:
+            path (`str`): Path of the model configuration YAML file.
+
+        Returns:
+            [`TGIModelConfig`]: Configuration for TGI endpoint model.
+        """
+        import yaml
+
+        with open(path, "r") as f:
+            config = yaml.safe_load(f)["model"]
+        return cls(**config["instance"])
+
 
 # inherit from InferenceEndpointModel instead of LightevalModel since they both use the same interface, and only overwrite
 # the client functions, since they use a different client.

tests/models/endpoints/test_tgi_model.py

@@ -0,0 +1,42 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from dataclasses import asdict
+
+import pytest
+
+from lighteval.models.endpoints.tgi_model import TGIModelConfig
+
+
+class TestTGIModelConfig:
+    @pytest.mark.parametrize(
+        "config_path, expected_config",
+        [
+            (
+                "examples/model_configs/tgi_model.yaml",
+                {"inference_server_address": "", "inference_server_auth": None, "model_id": None},
+            ),
+        ],
+    )
+    def test_from_path(self, config_path, expected_config):
+        config = TGIModelConfig.from_path(config_path)
+        assert asdict(config) == expected_config