Update README.md

README.md

@@ -42,7 +42,15 @@ quantized_by: TheBloke
 <!-- description start -->
 # Description
 
-This repo contains GPTQ model files for [Mistral AI_'s Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
+This repo contains **EXPERIMENTAL** GPTQ model files for [Mistral AI_'s Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
+
+## Requires AutoGPTQ PR
+
+These files were made with, and will currently only work with, this AutoGPTQ PR: https://github.com/LaaZa/AutoGPTQ/tree/Mixtral
+
+To test, please build AutoGPTQ from source using that PR.
+
+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.
 
@@ -50,7 +58,7 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
 <!-- repositories-available start -->
 ## Repositories available
 
-* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-AWQ)
+* AWQ coming soon
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ)
 * [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)
@@ -61,28 +69,9 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
 
 ```
 <s>[INST] {prompt} [/INST]
-
 ```
-
 <!-- 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
 
@@ -187,6 +176,8 @@ 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 CURRENTLY ONLY WORK WITH AUTOGPTQ LOADER, WITH AUTOGPTQ COMPILED FROM PR LISTED ABOVE**
+
 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.
@@ -210,44 +201,6 @@ 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/Mixtral-8x7B-Instruct-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"
-
-prompt = "Tell me about AI"
-prompt_template=f'''<s>[INST] {prompt} [/INST]
-'''
-
-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
 
@@ -276,17 +229,24 @@ pip3 install .
 ### Example Python code
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
 model_name_or_path = "TheBloke/Mixtral-8x7B-Instruct-v0.1-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")
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, GPTQConfig
+from auto_gptq import AutoGPTQForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+model_name_or_path = args.model_dir
+# To use a different branch, change revision
+# For example: revision="gptq-4bit-32g-actorder_True"
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        model_basename="model",
+        use_safetensors=True,
+        trust_remote_code=False,
+        device="cuda:0",
+        use_triton=False,
+        disable_exllama=False,
+        disable_exllamav2=True,
+        quantize_config=None)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True, trust_remote_code=False)
 
 prompt = "Tell me about AI"
 prompt_template=f'''<s>[INST] {prompt} [/INST]
@@ -297,36 +257,9 @@ 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 -->
-
 <!-- footer start -->
 <!-- 200823 -->
 ## Discord