diff --git a/README.md b/README.md
index f4c1fee..87612b8 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,9 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 Script to generate batches of random unique prompts to be used in the Compute Horde project synthetic jobs.
+
 The prompt that generates prompts is inspired from [Bittensor Subnet 18 (Cortex. t)] (https://github.com/Datura-ai/cortex.t/blob/276cfcf742e8b442500435a1c1862ac4dffa9e20/cortext/utils.py#L193) (licensed under the MIT License.)
+
 The generated prompts will be saved in `<output_folder_path>/prompts_<uuid>.txt`, each line of the text file containing a prompt.
 
 
diff --git a/pdm.lock b/pdm.lock
index 3838b6f..03789e1 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -611,13 +611,13 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "74.1.0"
+version = "74.1.1"
 requires_python = ">=3.8"
 summary = "Easily download, build, install, upgrade, and uninstall Python packages"
 groups = ["default"]
 files = [
-    {file = "setuptools-74.1.0-py3-none-any.whl", hash = "sha256:cee604bd76cc092355a4e43ec17aee5369095974f41f088676724dc6bc2c9ef8"},
-    {file = "setuptools-74.1.0.tar.gz", hash = "sha256:bea195a800f510ba3a2bc65645c88b7e016fe36709fefc58a880c4ae8a0138d7"},
+    {file = "setuptools-74.1.1-py3-none-any.whl", hash = "sha256:fc91b5f89e392ef5b77fe143b17e32f65d3024744fba66dc3afe07201684d766"},
+    {file = "setuptools-74.1.1.tar.gz", hash = "sha256:2353af060c06388be1cecbf5953dcdb1f38362f87a2356c480b6b4d5fcfc8847"},
 ]
 
 [[package]]
diff --git a/src/compute_horde_prompt_gen/model.py b/src/compute_horde_prompt_gen/model.py
index 4998646..65f1c4f 100644
--- a/src/compute_horde_prompt_gen/model.py
+++ b/src/compute_horde_prompt_gen/model.py
@@ -1,9 +1,4 @@
-import torch
 import logging
-from transformers import (
-    AutoTokenizer,
-    AutoModelForCausalLM,
-)
 
 from prompt import PROMPT_ENDING
 
@@ -15,7 +10,7 @@ def __init__(self):
         pass
 
     def generate(self, prompts: list[str], num_return_sequences: int, **_kwargs):
-        return torch.rand(len(prompts) * num_return_sequences)
+        return [1 for _ in range(len(prompts) * num_return_sequences)]
 
     def decode(self, _output):
         return f"COPY PASTE INPUT PROMPT {PROMPT_ENDING} Here is the list of prompts:\nHow are you?\nDescribe something\nCount to ten\n"
@@ -23,6 +18,12 @@ def decode(self, _output):
 
 class GenerativeModel:
     def __init__(self, model_path: str, quantize: bool = False):
+        import torch
+        from transformers import (
+            AutoTokenizer,
+            AutoModelForCausalLM,
+        )
+
         quantization_config = None
         if quantize:
             from transformers import BitsAndBytesConfig