Upload README.md

README.md

@@ -47,7 +47,7 @@ This repo contains GGUF format model files for [Eric Hartford's Wizardlm 30B Unc
 <!-- README_GGUF.md-about-gguf start -->
 ### About GGUF
 
-GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
+GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
 
 Here is an incomplate list of clients and libraries that are known to support GGUF:
 
@@ -86,7 +86,7 @@ Here is an incomplate list of clients and libraries that are known to support GG
 <!-- compatibility_gguf start -->
 ## Compatibility
 
-These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
+These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
 
 They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
 
@@ -150,7 +150,7 @@ 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:
@@ -179,25 +179,25 @@ 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/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-30B-uncensored-GGUF WizardLM-30B-Uncensored.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.
+Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
 </details>
 <!-- README_GGUF.md-how-to-download end -->
 
 <!-- README_GGUF.md-how-to-run start -->
 ## Example `llama.cpp` command
 
-Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
+Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 32 -m WizardLM-30B-Uncensored.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}\n### Response:"
+./main -ngl 32 -m WizardLM-30B-Uncensored.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}\n### Response:"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 
-Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
+Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
 
 If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
 
@@ -211,22 +211,24 @@ Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://git
 
 You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
 
-### How to load this model from Python using ctransformers
+### How to load this model in Python code, using ctransformers
 
 #### First install the package
 
-```bash
+Run one of the following commands, according to your system:
+
+```shell
 # Base ctransformers with no GPU acceleration
-pip install ctransformers>=0.2.24
+pip install ctransformers
 # Or with CUDA GPU acceleration
-pip install ctransformers[cuda]>=0.2.24
-# Or with ROCm GPU acceleration
-CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
-# Or with Metal GPU acceleration for macOS systems
-CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
+pip install ctransformers[cuda]
+# Or with AMD ROCm GPU acceleration (Linux only)
+CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
+# Or with Metal GPU acceleration for macOS systems only
+CT_METAL=1 pip install ctransformers --no-binary ctransformers
 ```
 
-#### Simple example code to load one of these GGUF models
+#### Simple ctransformers example code
 
 ```python
 from ctransformers import AutoModelForCausalLM
@@ -239,7 +241,7 @@ print(llm("AI is going to"))
 
 ## How to use with LangChain
 
-Here's guides on using llama-cpp-python or ctransformers with LangChain:
+Here are guides on using llama-cpp-python and ctransformers with LangChain:
 
 * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
 * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)