Upload README.md

README.md

@@ -78,15 +78,8 @@ A chat between a curious user and an artificial intelligence assistant. The assi
 ```
 
 <!-- prompt-template end -->
-<!-- licensing start -->
-## Licensing
 
-The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
 
-As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
-
-In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [VicUnlocked-30B-LoRA](https://huggingface.co/Neko-Institute-of-Science/VicUnLocked-30b-LoRA).
-<!-- licensing end -->
 <!-- compatibility_gguf start -->
 ## Compatibility
 
@@ -145,7 +138,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: TheBloke/VicUnlocked-30B-LoRA-GGUF and below it, a specific filename to download, such as: VicUnlocked-30B.q4_K_M.gguf.
+Under Download Model, you can enter the model repo: TheBloke/VicUnlocked-30B-LoRA-GGUF and below it, a specific filename to download, such as: VicUnlocked-30B.Q4_K_M.gguf.
 
 Then click Download.
 
@@ -154,13 +147,13 @@ Then click Download.
 I recommend using the `huggingface-hub` Python library:
 
 ```shell
-pip3 install huggingface-hub>=0.17.1
+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 TheBloke/VicUnlocked-30B-LoRA-GGUF VicUnlocked-30B.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download TheBloke/VicUnlocked-30B-LoRA-GGUF VicUnlocked-30B.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -183,7 +176,7 @@ pip3 install hf_transfer
 And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
 
 ```shell
-HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/VicUnlocked-30B-LoRA-GGUF VicUnlocked-30B.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/VicUnlocked-30B-LoRA-GGUF VicUnlocked-30B.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
@@ -196,7 +189,7 @@ Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running
 Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 32 -m VicUnlocked-30B.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"
+./main -ngl 32 -m VicUnlocked-30B.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -236,7 +229,7 @@ CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
 from ctransformers import AutoModelForCausalLM
 
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/VicUnlocked-30B-LoRA-GGUF", model_file="VicUnlocked-30B.q4_K_M.gguf", model_type="llama", gpu_layers=50)
+llm = AutoModelForCausalLM.from_pretrained("TheBloke/VicUnlocked-30B-LoRA-GGUF", model_file="VicUnlocked-30B.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
 
 print(llm("AI is going to"))
 ```