Update README.md (#2)

- Update README.md (ec73cdd4bbdf3c6413a2312fdc26908600cd8e30)

README.md

@@ -98,7 +98,7 @@ pip3 install huggingface-hub
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 
 ```shell
-huggingface-cli download MaziyarPanahi/Hermes-2-Pro-11B-GGUF Hermes-2-Pro-11B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download MaziyarPanahi/Hermes-2-Pro-11B-GGUF Hermes-2-Pro-11B.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 </details>
 <details>
@@ -121,7 +121,7 @@ pip3 install hf_transfer
 And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
 
 ```shell
-HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Hermes-2-Pro-11B-GGUF Hermes-2-Pro-11B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/Hermes-2-Pro-11B-GGUF Hermes-2-Pro-11B.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
@@ -132,7 +132,7 @@ Windows Command Line users: You can set the environment variable by running `set
 Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 35 -m Hermes-2-Pro-11B-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
+./main -ngl 35 -m Hermes-2-Pro-11B.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
 {system_message}<|im_end|>
 <|im_start|>user
 {prompt}<|im_end|>
@@ -209,7 +209,7 @@ output = llm(
 
 # Chat Completion API
 
-llm = Llama(model_path="./Hermes-2-Pro-11B-GGUF.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
+llm = Llama(model_path="./Hermes-2-Pro-11B.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
 llm.create_chat_completion(
     messages = [
         {"role": "system", "content": "You are a story writing assistant."},