Update README.md

README.md

@@ -42,31 +42,26 @@ quantized_by: TheBloke
 <!-- description start -->
 ## Description
 
-This repo contains GGUF format model files for [Mistral AI_'s Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
+This repo contains **EXPERIMENTAL** GGUF format model files for [Mistral AI_'s Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
 
-<!-- description end -->
-<!-- README_GGUF.md-about-gguf start -->
-### About GGUF
+## EXPERIMENTAL - REQUIRES LLAMA.CPP PR
+
+These are experimental GGUF files, created using a llama.cpp PR found here: https://github.com/ggerganov/llama.cpp/pull/4406.
+
+THEY WILL NOT WORK WITH LLAMA.CPP FROM `main`, OR ANY DOWNSTREAM LLAMA.CPP CLIENT - such as LM Studio, llama-cpp-python, text-generation-webui, etc.
 
-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.
+To test these GGUFs, please build llama.cpp from the above PR.
 
-Here is an incomplete list of clients and libraries that are known to support GGUF:
+I have tested CUDA acceleration and it works great. I have not yet tested other forms of GPU acceleration.
 
-* [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.
 
-<!-- README_GGUF.md-about-gguf end -->
+<!-- description end -->
+
 <!-- repositories-available start -->
 ## Repositories available
 
+* AWQ: coming soon
+* GPTQ: coming soon
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF)
 * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
 <!-- repositories-available end -->
@@ -76,36 +71,10 @@ Here is an incomplete list of clients and libraries that are known to support GG
 
 ```
 <s>[INST] {prompt} [/INST]
-
 ```
 
 <!-- prompt-template end -->
 
-
-<!-- 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)
-
-They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
-
-## Explanation of quantisation methods
-
-<details>
- <summary>Click to see details</summary>
-
-The new methods available are:
-
-* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
-* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
-* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
-* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
-* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
-
-Refer to the Provided Files table below to see what files use which methods, and how.
-</details>
-<!-- compatibility_gguf end -->
-
 <!-- README_GGUF.md-provided-files start -->
 ## Provided files
 
@@ -131,18 +100,6 @@ Refer to the Provided Files table below to see what files use which methods, and
 
 **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
 
-The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
-
-* LM Studio
-* LoLLMS Web UI
-* Faraday.dev
-
-### In `text-generation-webui`
-
-Under Download Model, you can enter the model repo: TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF and below it, a specific filename to download, such as: mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf.
-
-Then click Download.
-
 ### On the command line, including multiple files at once
 
 I recommend using the `huggingface-hub` Python library:
@@ -154,7 +111,7 @@ 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 TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download TheBloke/Mixtral-8x7B-v0.1-GGUF mixtral-8x7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -163,7 +120,7 @@ huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF mixtral-8x7b-i
 You can also download multiple files at once with a pattern:
 
 ```shell
-huggingface-cli download TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
+huggingface-cli download TheBloke/Mixtral-8x7B-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
 ```
 
 For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
@@ -177,7 +134,7 @@ 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 TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mixtral-8x7B-v0.1-GGUF mixtral-8x7b-v0.1.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.
@@ -187,10 +144,10 @@ Windows Command Line users: You can set the environment variable by running `set
 <!-- README_GGUF.md-how-to-run start -->
 ## Example `llama.cpp` command
 
-Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
+Make sure you are using `llama.cpp` from [PR 4406](https://github.com/ggerganov/llama.cpp/pull/4406)
 
 ```shell
-./main -ngl 35 -m mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"
+./main -ngl 35 -m mixtral-8x7b-v0.1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -203,82 +160,11 @@ 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 currently 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.
-
-### 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-8x7b-instruct-v0.1.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(
- "<s>[INST] {prompt} [/INST]", # 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-8x7b-instruct-v0.1.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:
-
-* [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 -->
+Not currently supported.
 
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