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@@ -308,9 +308,7 @@ python generation/main.py fit --config generation/confs/cli_lean4_novel_premises
 
 ### Theorem Proving Evaluation on LeanDojo Benchmark
 
-After the tactic generator is trained, we combine it with best-first search to prove theorems by interacting with Lean.
-
-The evaluation script takes Hugging Face model checkpoints (either local or remote) as input. For remote models, you can simply use their names, e.g., [kaiyuy/leandojo-lean4-tacgen-byt5-small](https://huggingface.co/kaiyuy/leandojo-lean4-tacgen-byt5-small). For locally trained models, you first need to convert them from PyTorch Ligthning checkpoints to Hugging Face checkpoints:
+After the tactic generator is trained, we combine it with best-first search to prove theorems by interacting with Lean. The evaluation script takes Hugging Face model checkpoints (either local or remote) as input. For remote models, you can simply use their names, e.g., [kaiyuy/leandojo-lean4-tacgen-byt5-small](https://huggingface.co/kaiyuy/leandojo-lean4-tacgen-byt5-small). For locally trained models, you first need to convert them from PyTorch Ligthning checkpoints to Hugging Face checkpoints:
 ```bash
 python scripts/convert_checkpoint.py generator --src $PATH_TO_GENERATOR_CHECKPOINT --dst ./leandojo-lean4-tacgen-byt5-small
 python scripts/convert_checkpoint.py retriever --src $PATH_TO_RETRIEVER_CHECKPOINT --dst ./leandojo-lean4-retriever-byt5-small