Update README.md

README.md

@@ -55,23 +55,22 @@ This repo contains GGUF format model files for [OpenOrca's Mixtral SlimOrca 8X7B
 
 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 incomplete list of clients and libraries that are known to support GGUF:
-
-* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
-* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
-* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
-* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
-* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
-* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
-* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
-* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
+**MIXTRAL GGUF SUPPORT**
+
+Known to work in:
+* llama.cpp as of December 13th
+* KoboldCpp 1.52 as later
+* LM Studio 0.2.9 and later
+
+Support for Mixtral was merged into Llama.cpp on December 13th.
+
+Other clients/libraries, not listed above, may not yet work.
 
 <!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
 ## Repositories available
 
+* AWQ coming soon
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-SlimOrca-8x7B-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-SlimOrca-8x7B-GGUF)
 * [OpenOrca's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Open-Orca/Mixtral-SlimOrca-8x7B)
@@ -86,7 +85,6 @@ Here is an incomplete list of clients and libraries that are known to support GG
 <|im_start|>user
 {prompt}<|im_end|>
 <|im_start|>assistant
-
 ```
 
 <!-- prompt-template end -->
@@ -95,7 +93,7 @@ Here is an incomplete 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 [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
+These quantised GGUFv2 files are compatible with llama.cpp from December 13th onwards.
 
 They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
 
@@ -213,80 +211,16 @@ For other parameters and how to use them, please refer to [the llama.cpp documen
 
 ## How to run in `text-generation-webui`
 
-Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
+Not yet supported
 
 ## How to run from Python code
 
-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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
+Not yet supported
 
 ### How to load this model in Python code, using llama-cpp-python
 
-For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
-
-#### First install the package
-
-Run one of the following commands, according to your system:
-
-```shell
-# Base ctransformers with no GPU acceleration
-pip install llama-cpp-python
-# With NVidia CUDA acceleration
-CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
-# Or with OpenBLAS acceleration
-CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
-# Or with CLBLast acceleration
-CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
-# Or with AMD ROCm GPU acceleration (Linux only)
-CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
-# Or with Metal GPU acceleration for macOS systems only
-CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
-
-# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
-$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
-pip install llama-cpp-python
-```
-
-#### Simple llama-cpp-python example code
-
-```python
-from llama_cpp import Llama
-
-# 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 = Llama(
-  model_path="./mixtral-slimorca-8x7b.Q4_K_M.gguf",  # Download the model file first
-  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
-  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
-  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
-)
-
-# Simple inference example
-output = llm(
-  "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
-  max_tokens=512,  # Generate up to 512 tokens
-  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
-  echo=True        # Whether to echo the prompt
-)
-
-# Chat Completion API
-
-llm = Llama(model_path="./mixtral-slimorca-8x7b.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."},
-        {
-            "role": "user",
-            "content": "Write a story about llamas."
-        }
-    ]
-)
-```
-
-## How to use with LangChain
-
-Here are guides on using llama-cpp-python and ctransformers with LangChain:
+Not yet supported
 
-* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
-* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
 
 <!-- README_GGUF.md-how-to-run end -->