Hf: minor egde cases (#1380)

* edge cases where variable might not be assigned.

* type hint

lm_eval/models/huggingface.py

@@ -108,8 +108,8 @@ class HFLM(LM):
             assert not parallelize, "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
             self._model = pretrained
             self._device = self._model.device
-
             self._config = self._model.config
+            gpus = 0
 
             if tokenizer:
                 assert isinstance(
@@ -372,7 +372,7 @@ class HFLM(LM):
 
     def _get_backend(
         self,
-        config: transformers.AutoConfig,
+        config: Union[transformers.PretrainedConfig, transformers.AutoConfig],
         backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
         trust_remote_code: Optional[bool] = False,
     ) -> None:
@@ -1059,6 +1059,7 @@ class HFLM(LM):
             return -len(toks), x[0]
 
         pbar = tqdm(total=len(requests), disable=(self.rank != 0))
+        adaptive_batch_size = None
         if self.batch_size == "auto":
             # using rolling window with maximum context
             print("Passed argument batch_size = auto. Detecting largest batch size")
@@ -1103,7 +1104,7 @@ class HFLM(LM):
                         )
             else:
                 raise ValueError(
-                    f"Expected `kwargs` to be of type `dict` but got {kwargs}"
+                    f"Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}"
                 )
             if not until:
                 until = [self.tok_decode(self.eot_token_id)]