diff --git a/fast_llm/data/preparator/gpt_memmap/config.py b/fast_llm/data/preparator/gpt_memmap/config.py
index 2c4311c3..0273d7ea 100644
--- a/fast_llm/data/preparator/gpt_memmap/config.py
+++ b/fast_llm/data/preparator/gpt_memmap/config.py
@@ -1,6 +1,7 @@
 import os
 import pathlib
 import typing
+import logging
 
 from fast_llm.config import Config, Field, FieldHint, check_field, config_class
 from fast_llm.data.config import TokenizerConfig
@@ -23,7 +24,7 @@
 MEMMAP_DTYPES_INV = {y: x for x, y in MEMMAP_DTYPES.items()}
 MEMMAP_INDEX_HEADER = b"MMIDIDX\x00\x00"
 
-
+logger = logging.getLogger(__name__)
 @config_class
 class GPTHuggingfaceDatasetConfig(Config):
     path: str = Field(
@@ -76,7 +77,6 @@ class GPTHuggingfaceDatasetConfig(Config):
         hint=FieldHint.optional,
     )
 
-
 @config_class
 class DatasetPreparatorDistributedConfig(Config):
     # TODO: Unify with fast_llm.engine.distributed.config.DistributedConfig
@@ -109,6 +109,47 @@ def _validate(self) -> None:
         super()._validate()
         Assert.in_range(self.rank, 0, self.world_size)
 
+@config_class
+class LossMaskSpansConfig(Config):
+    masking_column: str = Field(
+        default="",
+        desc="Field containing (input) character spans for loss masking",
+        hint=FieldHint.core,
+    )
+    loss_masking_spans: str = Field(
+        default="fast_llm_loss_masking_spans",
+        desc="Column name of field that would contain the masked spans.",
+        hint=FieldHint.optional,
+    )   
+    def _validate(self) -> None:
+        assert isinstance(self.loss_masking_spans, str), "loss_masking_spans col name must be a string."
+        super()._validate()
+
+@config_class
+class CombineFieldsConfig(Config):
+    col_names: typing.List[str] = Field(
+        default_factory=list,
+        desc="Fields of the dataset to combine.",
+        hint=FieldHint.core,
+    )
+    delimiter: str = Field(
+        default=" ",
+        desc="Delimiter to use when combining fields.",
+        hint=FieldHint.optional,
+    )
+    new_field_name: str = Field(
+        default="fast_llm_combined_field",
+        desc="Name of the new field to create.",
+        hint=FieldHint.optional,
+    )
+    set_masking_span: LossMaskSpansConfig = Field(
+        default_factory=LossMaskSpansConfig,
+        desc="Compute loss_masking_spans for the newly combined field.",
+        hint=FieldHint.optional,
+    )
+    def _validate(self) -> None:
+        assert isinstance(self.delimiter, str), "Delimiter must be a string."
+        super()._validate()
 
 @config_class()
 class GPTMemmapDatasetPreparatorConfig(DatasetPreparatorConfig):
@@ -164,11 +205,24 @@ class GPTMemmapDatasetPreparatorConfig(DatasetPreparatorConfig):
         " Does not shuffle samples.",
         hint=FieldHint.optional,
     )
+    combine_fields: CombineFieldsConfig = Field(
+        default_factory=CombineFieldsConfig,
+        desc="Combine all files into a single file.",
+        hint=FieldHint.optional,
+    )
 
     def _validate(self) -> None:
         assert self.tokenizer.path is not None
         if self.dataset.data_type is not None:
             Assert.incl(DataType.from_numpy(self.dataset.data_type.numpy), MEMMAP_DTYPES_INV)
+        if len(self.combine_fields.col_names) > 0:
+            logger.info(
+            f"Setting dataset.field to {self.combine_fields.new_field_name}",
+            )
+            self.dataset.field = self.combine_fields.new_field_name
+            if self.combine_fields.set_masking_span.masking_column != "":
+                logger.info(f"Setting dataset.loss_masking_spans to {self.combine_fields.set_masking_span.loss_masking_spans}")
+                self.dataset.loss_masking_spans = self.combine_fields.set_masking_span.loss_masking_spans
         super()._validate()
 
     @classmethod
diff --git a/fast_llm/data/preparator/gpt_memmap/prepare.py b/fast_llm/data/preparator/gpt_memmap/prepare.py
index 23e497bf..bc528005 100644
--- a/fast_llm/data/preparator/gpt_memmap/prepare.py
+++ b/fast_llm/data/preparator/gpt_memmap/prepare.py
@@ -208,6 +208,38 @@ def run(self) -> None:
                 torch.distributed.barrier()
 
         assert isinstance(dataset, datasets.Dataset)
+        
+        # Check for combining fields
+        if len(self._config.combine_fields.col_names) > 0: 
+            Assert.eq(len(set(self._config.combine_fields.col_names).intersection(dataset.column_names)), len(self._config.combine_fields.col_names),\
+                msg=f"Some columns to combine are not in the dataset. {set(self._config.combine_fields.col_names).difference(dataset.column_names)}")
+            
+            logger.info(f"Combining fields {self._config.combine_fields.col_names} into {self._config.combine_fields.new_field_name}")
+            dataset = dataset.map(
+                lambda example: {
+                    self._config.combine_fields.new_field_name: self._config.combine_fields.delimiter.join(
+                        str(example[column]) for column in self._config.combine_fields.col_names
+                    )
+                },
+                batched=False,
+                desc="Combining fields",
+            )
+            logger.info(f"Sample after combining fields:\n{dataset[0]}")
+            # Note: self.dataset.field is set to new_field_name for the rest of the operation see config validation
+            
+            if self._config.combine_fields.set_masking_span.masking_column != "":
+                Assert.incl(self._config.combine_fields.set_masking_span.masking_column, dataset.column_names)
+                dataset = dataset.map(
+                    lambda example: {
+                        self._config.dataset.loss_masking_spans: [
+                            (0, len(str(example[self._config.combine_fields.set_masking_span.masking_column])) - 1)
+                        ]# spans are inclusive
+                    },
+                    batched=False,
+                    desc="Setting loss masking spans",
+                )
+                logger.info(f"Sample after setting loss masking spans:\n{dataset[0]}")
+
         dataset = dataset.shard(
             num_shards=self._config.distributed.world_size,
             index=self._config.distributed.rank,