Update README.md

README.md

@@ -38,6 +38,14 @@ quantized_by: TheBloke
 
 This repo contains GPTQ model files for [Charles Goddard's Mixtralnt 4X7B Test](https://huggingface.co/chargoddard/mixtralnt-4x7b-test).
 
+## Requires AutoGPTQ PR + transformers 4.36.0
+
+These files were made with, and will currently only work with, this AutoGPTQ PR: https://github.com/LaaZa/AutoGPTQ/tree/Mixtral-fix
+
+To test, please build AutoGPTQ from source using that PR.  You also need Transformers version 4.36.0, released December 11th.
+
+Transformers support has just arrived also via two PRs - and is expected in main Transformers + Optimum tomorrow (Dec 12th).
+
 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 -->
@@ -60,22 +68,6 @@ 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
 
@@ -181,6 +173,8 @@ 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)
 
+**WILL ONLY WORK WITH TRANSFORMERS 4.36.0 PLUS AUTOGPTQ FROM FORK LISTED IN DESCRIPTION**
+
 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.
@@ -204,122 +198,7 @@ It is strongly recommended to use the text-generation-webui one-click-installers
 
 <!-- README_GPTQ.md-text-generation-webui end -->
 
-<!-- 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/mixtralnt-4x7b-test-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"
-
-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.
-
-```shell
-pip3 install --upgrade transformers optimum
-# If using PyTorch 2.1 + CUDA 12.x:
-pip3 install --upgrade auto-gptq
-# or, if using PyTorch 2.1 + CUDA 11.x:
-pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-```
-
-If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
-
-```shell
-pip3 uninstall -y auto-gptq
-git clone https://github.com/PanQiWei/AutoGPTQ
-cd AutoGPTQ
-git checkout v0.5.1
-pip3 install .
-```
-
-### Example Python code
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-model_name_or_path = "TheBloke/mixtralnt-4x7b-test-GPTQ"
-# To use a different branch, change revision
-# For example: revision="gptq-4bit-128g-actorder_True"
-model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
-                                             device_map="auto",
-                                             trust_remote_code=False,
-                                             revision="main")
-
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
-
-prompt = "Tell me about AI"
-prompt_template=f'''{prompt}
-'''
-
-print("\n\n*** Generate:")
-
-input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
-output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
-print(tokenizer.decode(output[0]))
-
-# Inference can also be done using transformers' pipeline
-
-print("*** Pipeline:")
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=512,
-    do_sample=True,
-    temperature=0.7,
-    top_p=0.95,
-    top_k=40,
-    repetition_penalty=1.1
-)
-
-print(pipe(prompt_template)[0]['generated_text'])
-```
-<!-- README_GPTQ.md-use-from-python end -->
-
-<!-- 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.
 
-For a list of clients/servers, please see "Known compatible clients / servers", above.
-<!-- README_GPTQ.md-compatibility end -->
 
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