Custom request headers | trust_remote_code param fix (#3069)

* added headers and custom model name | fixed bug with trust_remote_code param

* linting

* removed custom model name | changed headers override

* add `header` to base TemplateAPI

* nit

---------
Co-authored-by: Baber <baber@hey.com>

docs/API_guide.md

@@ -21,7 +21,11 @@ When subclassing `TemplateAPI`, you need to implement the following methods:
 1. `_create_payload`: Creates the JSON payload for API requests.
 2. `parse_logprobs`: Parses log probabilities from API responses.
 3. `parse_generations`: Parses generated text from API responses.
-4. `headers`: Returns the headers for the API request.
+
+Optional Properties:
+
+4. `header`: Returns the headers for the API request.
+5. `api_key`: Returns the API key for authentication (if required).
 
 You may also need to override other methods or properties depending on your API's specific requirements.
 
@@ -97,6 +101,10 @@ When initializing a `TemplateAPI` instance or a subclass, you can provide severa
   - Whether to validate the certificate of the API endpoint (if HTTPS).
   - Default is True.
 
+- `header` (dict, optional):
+  - Custom headers for API requests.
+  - If not provided, uses `{"Authorization": f"Bearer {self.api_key}"}` by default.
+
 Example usage:
 
 ```python

lm_eval/__main__.py

@@ -436,7 +436,10 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
 
         datasets.config.HF_DATASETS_TRUST_REMOTE_CODE = True
 
-        args.model_args = args.model_args + ",trust_remote_code=True"
+        if isinstance(args.model_args, dict):
+            args.model_args["trust_remote_code"] = True
+        else:
+            args.model_args = args.model_args + ",trust_remote_code=True"
     (
         eval_logger.info(f"Selected Tasks: {task_names}")
         if eval_logger.getEffectiveLevel() >= logging.INFO

lm_eval/models/api_models.py

@@ -135,6 +135,7 @@ class TemplateAPI(TemplateLM):
         eos_string: str = None,
         # timeout in seconds
         timeout: int = 300,
+        header: Optional[Dict[str, str]] = None,
         max_images: int = 1,
         **kwargs,
     ) -> None:
@@ -152,6 +153,7 @@ class TemplateAPI(TemplateLM):
         self.model = model or pretrained
         self.base_url = base_url
         self.tokenizer = tokenizer
+        self._header = header
         if not isinstance(batch_size, int) and "auto" in batch_size:
             eval_logger.warning(
                 "Automatic batch size is not supported for API models. Defaulting to batch size 1."
@@ -296,7 +298,7 @@ class TemplateAPI(TemplateLM):
     @cached_property
     def header(self) -> dict:
         """Override this property to return the headers for the API request."""
-        return {"Authorization": f"Bearer {self.api_key}"}
+        return self._header or {"Authorization": f"Bearer {self.api_key}"}
 
     @property
     def tokenizer_name(self) -> str:

lm_eval/models/openai_completions.py

@@ -16,8 +16,8 @@ eval_logger = logging.getLogger(__name__)
 class LocalCompletionsAPI(TemplateAPI):
     def __init__(
         self,
-        base_url=None,
-        tokenizer_backend="huggingface",
+        base_url: str = None,
+        tokenizer_backend: str = "huggingface",
         **kwargs,
     ):
         super().__init__(
@@ -108,9 +108,9 @@ class LocalCompletionsAPI(TemplateAPI):
 class LocalChatCompletion(LocalCompletionsAPI):
     def __init__(
         self,
-        base_url=None,
-        tokenizer_backend=None,
-        tokenized_requests=False,
+        base_url: str = None,
+        tokenizer_backend: str = None,
+        tokenized_requests: bool = False,
         **kwargs,
     ):
         eval_logger.warning(
@@ -236,6 +236,7 @@ class OpenAIChatCompletion(LocalChatCompletion):
             eval_logger.warning(
                 "o1 models do not support `stop` and only support temperature=1"
             )
+
         super().__init__(
             base_url=base_url,
             tokenizer_backend=tokenizer_backend,