Update README.md

README.md

@@ -34,16 +34,16 @@ pip install -U "huggingface_hub[cli]"
 Then, you can target the specific file you want:
 
 ```
-huggingface-cli download bartowski/gemma-2-9b-it-GGUF --include "gemma-2-9b-it-Q4_K_M.gguf" --local-dir ./
+huggingface-cli download jodog0412/gemma-2-27b-it-Q4_K_M --include "gemma-2-instruct-Q4_K_M.gguf" --local-dir ./
 ```
 
 If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
 
 ```
-huggingface-cli download bartowski/gemma-2-9b-it-GGUF --include "gemma-2-9b-it-Q8_0.gguf/*" --local-dir gemma-2-9b-it-Q8_0
+huggingface-cli download jodog0412/gemma-2-27b-it-Q4_K_M --include "gemma-2-instruct-Q4_K_M.gguf/*" --local-dir gemma-2-instruct-Q4_K_M
 ```
 
-You can either specify a new local-dir (gemma-2-9b-it-Q8_0) or download them all in place (./)
+You can either specify a new local-dir (gemma-2-instruct-Q4_K_M) or download them all in place (./)
 ## Which file should I choose?
 A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
 The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.