Update README.md

README.md

@@ -53,11 +53,42 @@ quantized_by: TheBloke
 - Model creator: [Disco Research](https://huggingface.co/DiscoResearch)
 - Original model: [Discolm Mixtral 8X7B v2](https://huggingface.co/DiscoResearch/DiscoLM-mixtral-8x7b-v2)
 
+# WARNING - I CAN'T GET THESE GPTQ QUANTS TO WORK
+
+Unfortunately, after 10 hours quanting at not insignificant cost, they don't actually appear to work.
+
+I will leave them up in case any solution presents itself soon.  But for now, I get errors like this
+
+```
+  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/auto_gptq/nn_modules/qlinear/qlinear_cuda_old.py", line 239, in forward
+    zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
+RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 1, 0] because the unspecified dimension size -1 can be any value and is ambiguous
+
+  File "/workspace/venv/pytorch2/lib/python3.10/site-packages/auto_gptq/nn_modules/qlinear/qlinear_cuda.py", line 245, in forward
+    zeros = zeros.reshape(self.scales.shape)
+RuntimeError: shape '[32, 8]' is invalid for input of size 0
+```
+
 <!-- description start -->
 # Description
 
 This repo contains GPTQ model files for [Disco Research's Discolm Mixtral 8X7B v2](https://huggingface.co/DiscoResearch/DiscoLM-mixtral-8x7b-v2).
 
+**Experimental model**
+
+This is an experimental GPTQ of MistralAI's Mixtral 7B 8Expert.
+
+This is a quantisation of an unofficial implementation of Mixtral 7B 8Experted, created and hosted by DiscoResearch at: [DiscoResearch/mixtral-7b-8expert](https://huggingface.co/DiscoResearch/mixtral-7b-8expert).
+
+To use it requires:
+* Latest Transformers, installed from Github:
+```
+pip3 install git+https://github.com/huggingface/transformers.git
+```
+* `trust_remote_code=True`
+
+Note that I have not yet tested the model myself, I will update when I know VRAM requirements.
+
 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 -->
@@ -83,22 +114,6 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
 <!-- prompt-template end -->
 
 
-
-<!-- README_GPTQ.md-compatible clients start -->
-## Known compatible clients / servers
-
-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)
-
-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 -->
 ## Provided files, and GPTQ parameters
 
@@ -124,8 +139,8 @@ 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](https://huggingface.co/TheBloke/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.97 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | 
-| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.00 GB | Yes | 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/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | 
+| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.00 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/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.00 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/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.98 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/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-3bit-128g-actorder_true) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.00 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | 
 | [gptq-3bit-32g-actorder_true](https://huggingface.co/TheBloke/DiscoLM-mixtral-8x7b-v2-GPTQ/tree/gptq-3bit-32g-actorder_true) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.99 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. | 
@@ -204,6 +219,8 @@ 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** This likely doesn't work at the moment.
+
 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.
@@ -230,6 +247,8 @@ 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)
 
+**NOTE** This likely doesn't work at the moment.
+
 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:
@@ -272,6 +291,8 @@ print(f"Model output: {response}")
 <!-- README_GPTQ.md-use-from-python start -->
 ## Python code example: inference from this GPTQ model
 
+**NOTE** I can't get this working yet.
+
 ### Install the necessary packages
 
 Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
@@ -345,11 +366,8 @@ 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.
+These GPTQs are not yet working.
 
-For a list of clients/servers, please see "Known compatible clients / servers", above.
 <!-- README_GPTQ.md-compatibility end -->
 
 <!-- footer start -->
@@ -362,8 +380,6 @@ For further support, and discussions on these models and AI in general, join us
 
 ## Thanks, and how to contribute
 
-Thanks to the [chirper.ai](https://chirper.ai) team!
-
 Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
 
 I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.