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license: apache-2.0
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First install [Lean 4](https://leanprover-community.github.io/get_started.html). Then clone this repo:

`git clone --recurse-submodules https://huggingface.co/datasets/elohn/miniCodeProps` 

The outer LeanSrc folder is a [Lean Project](https://leanprover-community.github.io/install/project.html). You can open that folder directly in VSCode and check that the proofs in `LeanSrc/Sorts.lean` type check after following the instructions for working on an existing lean project in the Lean 4 documentation.
The main miniCodeProps folder handles extracting the benchmark and calculating baselines. If anything fails when building Lean or running `lake exe cache get` from LeanSrc, the [Zulip Chat](https://leanprover.zulipchat.com/) is the best resource for troubleshooting.

After cloning the repo, you will need to install [Lean REPL](https://github.com/leanprover-community/repl). By default, our scripts expect the `repl` folder to be directly inside the miniCodeProps folder. run    `lake build`    from within the `repl` folder.

The `extract.py` script is used only to create the json-formatted benchmark.

The `baseline.py` script contains the code we used to get our baseline results. It shows how to interact with Lean Repl programmatically, although some interactions are still somewhat buggy in that the repl will send i.e. an extra newline or weirdly formatted message that requires our script to restart the repl.
Regardless, if you would like to use our setup, We ran our baselines using [LLMStep](https://github.com/wellecks/llmstep). However, our code also includes a natural place to write your own function to generate tactics given the goal and file context (see `get_tactics_llmstep` in `baseline.py`). We modified the LLMStep server to return average suggestion log-probabilities per suggestion to implement best-first search; we will publish our fork of that soon as well.