Upload README.md

README.md

@@ -44,12 +44,18 @@ quantized_by: TheBloke
 
 This repo contains GPTQ model files for [Mistral AI_'s Mixtral 8X7B v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).
 
+Mixtral GPTQs currently require:
+* Transformers 4.36.0 or later
+* either, AutoGPTQ 0.6 compiled from source, or
+* Transformers 4.37.0.dev0 compiled from Github with: `pip3 install git+https://github.com/huggingface/transformers`
+
 Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
 
 <!-- description end -->
 <!-- repositories-available start -->
 ## Repositories available
 
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/mixtral-8x7b-v0.1-AWQ)
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-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-v0.1)
@@ -72,14 +78,8 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
 
 GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
 
-These GPTQ models are known to work in the following inference servers/webuis.
-
-- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
-- [KoboldAI United](https://github.com/henk717/koboldai)
-- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
-- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
+Mixtral GPTQs currently have special requirements - see Description above.
 
-This may not be a complete list; if you know of others, please let me know!
 <!-- README_GPTQ.md-compatible clients end -->
 
 <!-- README_GPTQ.md-provided-files start -->
@@ -106,13 +106,13 @@ Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with T
 
 | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
 | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
-| main | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 23.81 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
-| gptq-4bit-128g-actorder_True | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 24.70 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
-| gptq-4bit-32g-actorder_True | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 27.42 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
-| gptq-3bit--1g-actorder_True | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.01 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | 
-| gptq-3bit-128g-actorder_True | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.85 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | 
-| gptq-8bit--1g-actorder_True | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 47.04 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | 
-| gptq-8bit-128g-actorder_True | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.10 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
+| [main](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 23.81 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
+| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 24.70 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | 
+| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 27.42 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
+| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.01 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | 
+| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.85 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | 
+| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 47.04 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | 
+| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 48.10 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
 
 <!-- README_GPTQ.md-provided-files end -->
 
@@ -186,6 +186,12 @@ Note that using Git with HF repos is strongly discouraged. It will be much slowe
 <!-- README_GPTQ.md-text-generation-webui start -->
 ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
 
+**NOTE**: Requires:
+
+* Transformers 4.36.0, or Transformers 4.37.0.dev0 from Github
+* Either AutoGPTQ 0.6 compiled from source and `Loader: AutoGPTQ`,
+* or, `Loader: Transformers`, if you installed Transformers from Github: `pip3 install git+https://github.com/huggingface/transformers`
+
 Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
 
 It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
@@ -212,50 +218,18 @@ It is strongly recommended to use the text-generation-webui one-click-installers
 <!-- README_GPTQ.md-use-from-tgi start -->
 ## Serving this model from Text Generation Inference (TGI)
 
-It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
-
-Example Docker parameters:
-
-```shell
---model-id TheBloke/Mixtral-8x7B-v0.1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
-```
-
-Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
-
-```shell
-pip3 install huggingface-hub
-```
-
-```python
-from huggingface_hub import InferenceClient
-
-endpoint_url = "https://your-endpoint-url-here"
+Not currently supported for Mixtral models.
 
-prompt = "Tell me about AI"
-prompt_template=f'''{prompt}
-'''
-
-client = InferenceClient(endpoint_url)
-response = client.text_generation(prompt,
-                                  max_new_tokens=128,
-                                  do_sample=True,
-                                  temperature=0.7,
-                                  top_p=0.95,
-                                  top_k=40,
-                                  repetition_penalty=1.1)
-
-print(f"Model output: {response}")
-```
 <!-- README_GPTQ.md-use-from-tgi end -->
 <!-- README_GPTQ.md-use-from-python start -->
 ## Python code example: inference from this GPTQ model
 
 ### Install the necessary packages
 
-Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
+Requires: Transformers 4.37.0.dev0 from Github, Optimum 1.16.0 or later, and AutoGPTQ 0.5.1 or later.
 
 ```shell
-pip3 install --upgrade transformers optimum
+pip3 install --upgrade "git+https://github.com/huggingface/transformers" optimum
 # If using PyTorch 2.1 + CUDA 12.x:
 pip3 install --upgrade auto-gptq
 # or, if using PyTorch 2.1 + CUDA 11.x:
@@ -268,8 +242,7 @@ If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Lik
 pip3 uninstall -y auto-gptq
 git clone https://github.com/PanQiWei/AutoGPTQ
 cd AutoGPTQ
-git checkout v0.5.1
-pip3 install .
+DISABLE_QIGEN=1 pip3 install .
 ```
 
 ### Example Python code
@@ -287,7 +260,8 @@ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
-prompt = "Tell me about AI"
+prompt = "Write a story about llamas"
+system_message = "You are a story writing assistant"
 prompt_template=f'''{prompt}
 '''
 
@@ -319,11 +293,8 @@ print(pipe(prompt_template)[0]['generated_text'])
 <!-- README_GPTQ.md-compatibility start -->
 ## Compatibility
 
-The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
-
-[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
+The files provided are tested to work with AutoGPTQ 0.6 (compiled from source) and Transformers 4.37.0 (installed from Github).
 
-For a list of clients/servers, please see "Known compatible clients / servers", above.
 <!-- README_GPTQ.md-compatibility end -->
 
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