diff --git a/manifest/api/models/huggingface.py b/manifest/api/models/huggingface.py
index b442bd3..3a8c35f 100644
--- a/manifest/api/models/huggingface.py
+++ b/manifest/api/models/huggingface.py
@@ -47,11 +47,12 @@ class Pipeline:
     ):
         """Initialize."""
         self.model = model
+        config = model.config  # type: ignore
         # Used for GPT
-        self.max_length = getattr(model.config, "max_position_embeddings", None)
+        self.max_length = getattr(config, "max_position_embeddings", None)
         if self.max_length is None:
             # Used for T0
-            self.max_length = model.config.d_model
+            self.max_length = config.d_model
         self.tokenizer = tokenizer
         self.device = (
             torch.device("cpu")
@@ -341,26 +342,26 @@ class HuggingFaceModel(Model):
             tokenized_targets[k]["attention_mask"] for k in range(len(gold_choices))
         ]
         # Convert to tensors
+        tensor_features = {}
         for k in features:
-            features[k] = torch.LongTensor(features[k]).to(self.pipeline.device)
-            print(k, features[k].shape)
+            tensor_features[k] = torch.LongTensor(features[k]).to(self.pipeline.device)
         # Reduce GPU memory by feeding one at a time
         logits = [
-            self.pipeline.model(
-                input_ids=features["input_ids"][bs].unsqueeze(0),
-                attention_mask=features["attention_mask"][bs].unsqueeze(0),
-                labels=features["labels"][bs].unsqueeze(0),
+            self.pipeline.model(  # type: ignore
+                input_ids=tensor_features["input_ids"][bs].unsqueeze(0),
+                attention_mask=tensor_features["attention_mask"][bs].unsqueeze(0),
+                labels=tensor_features["labels"][bs].unsqueeze(0),
             ).logits
-            for bs in range(len(features["input_ids"]))
+            for bs in range(len(tensor_features["input_ids"]))
         ]
-        logits = torch.vstack(logits)
+        stacked_logits = torch.vstack(logits)
         # Compute most likely option
-        masked_log_probs = features["labels_attention_mask"].unsqueeze(
+        masked_log_probs = tensor_features["labels_attention_mask"].unsqueeze(
             -1
-        ) * torch.log_softmax(logits, dim=-1)
+        ) * torch.log_softmax(stacked_logits, dim=-1)
         seq_token_log_probs = torch.gather(
-            masked_log_probs, -1, features["labels"].unsqueeze(-1)
+            masked_log_probs, -1, tensor_features["labels"].unsqueeze(-1)
         )
         seq_log_prob = seq_token_log_probs.squeeze(dim=-1).sum(dim=-1)
         prediction = seq_log_prob.argmax(dim=-1).item()
-        return gold_choices[prediction]
+        return gold_choices[int(prediction)]
diff --git a/manifest/manifest.py b/manifest/manifest.py
index b417d28..699ce1f 100644
--- a/manifest/manifest.py
+++ b/manifest/manifest.py
@@ -1,6 +1,6 @@
 """Manifest class."""
 import logging
-from typing import Any, Iterable, List, Optional, Tuple, Union
+from typing import Any, Iterable, List, Optional, Tuple, Union, cast
 
 from tqdm.auto import tqdm
 
@@ -134,9 +134,9 @@ class Manifest:
             possible_request, full_kwargs = self.client.get_request(prompt_str, kwargs)
         else:
             try:
-                possible_request, full_kwargs = self.client.get_choice_logit_request(
-                    prompt_str, gold_choices, kwargs
-                )
+                possible_request, full_kwargs = cast(
+                    HuggingFaceClient, self.client
+                ).get_choice_logit_request(prompt_str, gold_choices, kwargs)
             except AttributeError:
                 raise ValueError("`gold_choices` only supported for HF models.")
         if len(kwargs) > 0: