fixes

src/lighteval/main_nanotron.py

@@ -54,7 +54,11 @@ def main(
             raise ValueError("The checkpoint path should point to a YAML file")
 
         nanotron_config: config_cls = get_config_from_file(
-            local_config_path, config_class=config_cls, model_config_class=model_config_cls
+            local_config_path,
+            config_class=config_cls,
+            model_config_class=model_config_cls,
+            skip_unused_config_keys=True,
+            skip_null_keys=True,
         )
 
         if lighteval_config_path:

src/lighteval/models/nanotron_model.py

@@ -1111,7 +1111,7 @@ def greedy_until(
         # automatic (variable) batch size detection for vectorization
         # pull longest context sample from request
         for request in requests:
-            request.stop_sequence = request.stop_sequence + [self.tokenizer.eos_token]
+            request.stop_sequence = request.stop_sequence + (self.tokenizer.eos_token,)
             request.tokenized_context = self.tok_encode(request.context)
 
         dataset = GenerativeTaskDatasetNanotron(requests=requests, dataset_splits=dataset_splits)
@@ -1134,8 +1134,8 @@ def greedy_until(
             dataset.split_start = subset_start
             dataset.split_end = min(subset_start + subset_length, total_length)
 
-            context_enc = dataset[0].tokenized_context
-            max_gen = max(item.generation_size for item in dataset)
+            context_enc = dataset[0][1].tokenized_context
+            max_gen = max(item[1].generation_size for item in dataset)
             max_input_length = min(len(context_enc) + max_gen, self.max_length)
             batch_size = self._get_batch_size(
                 override_bs=override_bs, max_input_length=max_input_length, starting_batch_size=starting_batch_size
@@ -1172,7 +1172,7 @@ def greedy_until(
                     )
                 iteration_start_time = time.time()
                 example_index, batch_data = zip(*all_batch)
-                context = [c.context for c in batch_data]
+                context = [c.tokenized_context for c in batch_data]
                 # we take the longest asked generation in the batch
                 # Multiple request may have different max generation length
                 max_tokens = max(d.generation_size for d in batch_data)  # d[1][1]
@@ -1244,7 +1244,7 @@ def greedy_until(
                     ):
                         # Ensure the generated responses do not contain the stop sequences.
                         decoded_response = self.tokenizer.decode(generation, skip_special_tokens=False)
-                        stop_terms = dataset[example_index][1][1][0]
+                        stop_terms = dataset[example_index][1].stop_sequence
                         for stop_term in stop_terms:
                             decoded_response = decoded_response.split(stop_term)[0]
                         # partial caching