mock no need torch

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.
 
 

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
 
@@ -15,14 +10,20 @@ 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"
 
 
 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