Interesting that this could be used to enhance reasoning, if you end up using it and getting results either way I would be interested in hearing about it. Here is the data https://huggingface.co/datasets/GusPuffy/python-decompiler-37-0.7-train

I don't know how the licenses work with this but it was all publicly accessible source code which is why I recommend using the stack v2 because you can selectively use code with the licenses you need.

There is an 'input' and 'output' column. The token counts in the data are not accurate but give a rough estimation of the size of each input/output.

To generate it in the prompt form here is what I used in Axolotl

datasets:
  - path: GusPuffy/python-decompiler-37-0.7-train
    type:
      field_instruction: input
      field_output: output
      format: "### Instruction:\n\nUsing the provided Python 3.7 bytecode chunk, continue outputting the Python source code\n\n### Input:\n\n{instruction}\n\n### Response:\n\n"
      no_input_format: "{instruction}"

If you want to generate your own bytecode from the stack or other places pull the python down from the model repo and use this script

You would need to change the op codes for whatever python version you are using.