Upload README.md

README.md

@@ -7,7 +7,7 @@ license: apache-2.0
 model_creator: YeungNLP
 model_name: Firefly Mixtral 8X7B
 model_type: mixtral
-prompt_template: '{prompt}
+prompt_template: '[INST] {prompt} [/INST]
 
   '
 quantized_by: TheBloke
@@ -40,27 +40,23 @@ quantized_by: TheBloke
 
 This repo contains GPTQ model files for [YeungNLP's Firefly Mixtral 8X7B](https://huggingface.co/YeungNLP/firefly-mixtral-8x7b).
 
-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/firefly-mixtral-8x7b-AWQ)
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/firefly-mixtral-8x7b-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/firefly-mixtral-8x7b-GGUF)
 * [YeungNLP's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/YeungNLP/firefly-mixtral-8x7b)
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
-## Prompt template: None
+## Prompt template: Mistral
 
 ```
-{prompt}
+[INST] {prompt} [/INST]
 
 ```
 
@@ -73,8 +69,14 @@ 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.
 
-Mixtral GPTQs currently have special requirements - see Description above.
+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 -->
@@ -182,12 +184,6 @@ 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.
@@ -214,18 +210,50 @@ 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)
 
-Not currently supported for Mixtral models.
+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/firefly-mixtral-8x7b-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'''[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
 
 ### Install the necessary packages
 
-Requires: Transformers 4.37.0.dev0 from Github, Optimum 1.16.0 or later, and AutoGPTQ 0.5.1 or later.
+Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
 
 ```shell
-pip3 install --upgrade "git+https://github.com/huggingface/transformers" optimum
+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:
@@ -238,7 +266,8 @@ 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
-DISABLE_QIGEN=1 pip3 install .
+git checkout v0.5.1
+pip3 install .
 ```
 
 ### Example Python code
@@ -258,7 +287,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
 prompt = "Write a story about llamas"
 system_message = "You are a story writing assistant"
-prompt_template=f'''{prompt}
+prompt_template=f'''[INST] {prompt} [/INST]
 '''
 
 print("\n\n*** Generate:")
@@ -289,8 +318,11 @@ print(pipe(prompt_template)[0]['generated_text'])
 <!-- README_GPTQ.md-compatibility start -->
 ## Compatibility
 
-The files provided are tested to work with AutoGPTQ 0.6 (compiled from source) and Transformers 4.37.0 (installed from Github).
+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 architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) 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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