diff --git a/README.md b/README.md
index d61c2de..a4457c4 100644
--- a/README.md
+++ b/README.md
@@ -75,7 +75,7 @@ cmake --build . --config Release
 
 #### Option 1: Download pre-quantized MiniGPT4 model
 
-Pre-quantized models are avaliable on Hugging Face ~ [7B](https://huggingface.co/datasets/maknee/minigpt4-7b-ggml/tree/main) or [13B](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/tree/main).
+Pre-quantized models are available on Hugging Face ~ [7B](https://huggingface.co/datasets/maknee/minigpt4-7b-ggml/tree/main) or [13B](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/tree/main).
 
 Recommended for reliable results, but slow inference speed: [minigpt4-13B-f16.bin](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml/blob/main/minigpt4-13B-f16.bin)
 
@@ -129,7 +129,7 @@ python convert.py ~/Downloads/pretrained_minigpt4.pth --outtype f16
 
 #### Option 1: Download pre-quantized vicuna-v0 model
 
-Pre-quantized models are avaliable on [Hugging Face](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/tree/main)
+Pre-quantized models are available on [Hugging Face](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/tree/main)
 
 Recommended for reliable results and decent inference speed: [ggml-vicuna-13B-v0-q5_k.bin](https://huggingface.co/datasets/maknee/ggml-vicuna-v0-quantized/blob/main/ggml-vicuna-13B-v0-q5_k.bin)