diff --git a/model/model_training/configs/config.yaml b/model/model_training/configs/config.yaml
index af0d67fb93..610a5ee239 100644
--- a/model/model_training/configs/config.yaml
+++ b/model/model_training/configs/config.yaml
@@ -88,6 +88,7 @@ defaults:
   deepspeed_config: configs/zero_config.json
   peft_model: false
   peft_type: "lora"
+  superhot: false
 
 use_system_tag:
   use_system_tag: True
diff --git a/model/model_training/custom_datasets/__init__.py b/model/model_training/custom_datasets/__init__.py
index e3c3eb3882..740fae8563 100644
--- a/model/model_training/custom_datasets/__init__.py
+++ b/model/model_training/custom_datasets/__init__.py
@@ -7,7 +7,7 @@
 from model_training.custom_datasets.extra_rm_datasets import load_anthropic_rlhf, load_hellaswag, load_shp
 from model_training.custom_datasets.instruction import INSTRUCTION_DATASETS, InstructionDataset
 from model_training.custom_datasets.oasst_dataset import load_oasst_export
-from model_training.custom_datasets.pretrain_datasets import RedPajama
+from model_training.custom_datasets.pretrain_datasets import FanFics, RedPajama
 from model_training.custom_datasets.prompt_dialogue import Gpt4All, OrcaChat, load_oig_file
 from model_training.custom_datasets.qa_datasets import (
     SODA,
@@ -170,6 +170,8 @@ def get_one_dataset(
         dataset = AlpacaGpt4(cache_dir=data_path, mode=mode, **kwargs)
     elif dataset_name == "red_pajama":
         dataset = RedPajama(cache_dir=data_path, mode=mode, **kwargs)
+    elif dataset_name == "fanfics":
+        dataset = FanFics(cache_dir=data_path, mode=mode, **kwargs)
     elif dataset_name == "gpteacher_roleplay":
         dataset = GPTeacher_Roleplay(cache_dir=data_path, mode=mode, **kwargs)
     elif dataset_name == "orca-chat":
diff --git a/model/model_training/custom_datasets/pretrain_datasets.py b/model/model_training/custom_datasets/pretrain_datasets.py
index eb067a6b36..fead0719b5 100644
--- a/model/model_training/custom_datasets/pretrain_datasets.py
+++ b/model/model_training/custom_datasets/pretrain_datasets.py
@@ -1,7 +1,9 @@
 """
    Datasets for LM objective pre-training aimed to prevent catastrophic forgetting during fine-tuning
 """
+import random
 from pathlib import Path
+from typing import Optional
 
 from datasets import load_dataset
 from model_training.custom_datasets.formatting import DatasetEntryLm
@@ -11,17 +13,54 @@
 class RedPajama(Dataset):
     name = "red_pajama"
 
-    def __init__(self, cache_dir: str | Path, mode: str = "sft", char_max_len: str = 9216) -> None:
+    def __init__(
+        self,
+        cache_dir: str | Path,
+        mode: str = "sft",
+        char_max_len: Optional[int] = 65536,
+        random_offset: bool = False,
+    ) -> None:
         super().__init__()
         self.mode = mode
         assert mode in ("sft", "rm", "rl")
         self.char_max_len = char_max_len
-
+        self.random_offset = random_offset
         self.dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=cache_dir)["train"]
 
     def __len__(self) -> int:
         return len(self.dataset)
 
     def __getitem__(self, index) -> DatasetEntryLm:
-        dialogue = DatasetEntryLm(text=self.dataset[index]["text"][: self.char_max_len])
-        return dialogue
+        text = self.dataset[index]["text"]
+        if self.char_max_len and len(text) > self.char_max_len:
+            offset = 0 if not self.random_offset else random.randrange(len(text) - self.char_max_len)
+            text = text[offset : offset + self.char_max_len]
+        return DatasetEntryLm(text=text)
+
+
+class FanFics(Dataset):
+    name = "fanfics"
+
+    def __init__(
+        self,
+        cache_dir: str | Path,
+        mode: str = "sft",
+        char_max_len: Optional[int] = 65536,
+        random_offset: bool = False,
+    ) -> None:
+        super().__init__()
+        self.mode = mode
+        assert mode in ("sft", "rm", "rl")
+        self.char_max_len = char_max_len
+        self.random_offset = random_offset
+        self.dataset = load_dataset("atom-in-the-universe/fanfics-10k-50k", cache_dir=cache_dir)["train"]
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __getitem__(self, index) -> DatasetEntryLm:
+        text = self.dataset[index]["TEXT"]
+        if self.char_max_len and len(text) > self.char_max_len:
+            offset = 0 if not self.random_offset else random.randrange(len(text) - self.char_max_len)
+            text = text[offset : offset + self.char_max_len]
+        return DatasetEntryLm(text=text)