Update README.md

README.md

@@ -15,7 +15,7 @@ We notice that, 1-bit quantization doesn't work well when applied directly on sm
 Note that the weights here are unsigned 1-bit (0 or 1), <a href="https://arxiv.org/abs/2402.17764">not ternary like the recent 1.58-bit work </a>. This is a more challenging task since we lose the sign of the weights and only fine-tune a small fraction of the parameters (~94MB worth of weights). 
 The dequantization step can be rewriten as a 1-bit matmul which could potentially require only additions + a very low-rank matmul which is fast to compute. 
 
-This versions offloads the meta-data to the CPU, so only the binary weights and the low-rank adapters are stored in the GPU memory. 
+This versions offload the meta-data to the CPU, so only the binary weights and the low-rank adapters are stored in the GPU memory. 
 
 ## Datasets
 The adapter was trained via SFT on random subsets of the following: