Update README.md

README.md

@@ -2,7 +2,7 @@
 license: apache-2.0
 ---
 # Trained Sparse Autoencoders on Pythia 2.8B
-I trained SAEs on the MLP_out activations of the Pythia 2.8B dataset. I trained using https://github.com/magikarp01/facts-sae.git, a fork of https://github.com/saprmarks/dictionary_learning designed for efficient multi-GPU (not yet multinode) training. I have checkpoints saved every 10k steps, but I have not uploaded them all: message me if you want more checkpoints.
+I trained SAEs on the MLP_out activations of the Pythia 2.8B dataset. I trained using github.com/magikarp01/facts-sae, a fork of github.com/saprmarks/dictionary_learning designed for efficient multi-GPU (not yet multinode) training. I have checkpoints saved every 10k steps, but I have not uploaded them all: message me if you want more intermediate checkpoints.
 
 The goal was originally to analyze these SAEs specifically to determine how well they contribute to performance on a [Sports Facts](https://www.lesswrong.com/posts/iGuwZTHWb6DFY3sKB/fact-finding-attempting-to-reverse-engineer-factual-recall) dataset. 
 I'm currently working on some other projects so I haven't actually had time to do this, but hopefully in the future some results might come out of these SAEs.