Merge remote-tracking branch 'refs/remotes/origin/main'

README.md

@@ -97,7 +97,7 @@ cd text-generation-webui
 pip install -r requirements.txt
 ```
 
-#### 4. Install GPTQ-for-LLaMa and the monkey patch
+#### 4. Install GPTQ
 
 The base installation covers [transformers](https://github.com/huggingface/transformers) models (`AutoModelForCausalLM` and `AutoModelForSeq2SeqLM` specifically) and [llama.cpp](https://github.com/ggerganov/llama.cpp) (GGML) models.
 

docs/GPTQ-models-(4-bit-mode).md

@@ -1,12 +1,56 @@
-In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
-
-This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa
-
 GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
 
-## GPTQ-for-LLaMa branches
+## AutoGPTQ
 
-Different branches of GPTQ-for-LLaMa are available:
+AutoGPTQ is the recommended way to create new quantized models: https://github.com/PanQiWei/AutoGPTQ
+
+### Installation
+
+To load a model quantized with AutoGPTQ in the web UI, manual installation is currently necessary:
+
+```
+conda activate textgen
+git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
+pip install .
+```
+
+You are going to need to have `nvcc` installed (see the [instructions below](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-0-install-nvcc)).
+
+### Usage
+
+Place the output folder generated by AutoGPTQ in your `models/` folder and load it with the `--autogptq` flag:
+
+```
+python server.py --autogptq --model model_name
+```
+
+Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
+
+### Offloading
+
+In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA (more on that below).
+
+For CPU offloading:
+
+```
+python server.py --autogptq --gpu-memory 3000MiB --model model_name
+```
+
+For multi-GPU:
+
+```
+python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
+```
+
+### Using LoRAs with AutoGPTQ
+
+Not supported yet.
+
+## GPTQ-for-LLaMa
+
+GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made by [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa) in this repository: https://github.com/qwopqwop200/GPTQ-for-LLaMa
+
+Different branches of GPTQ-for-LLaMa are currently available, including:
 
 | Branch | Comment |
 |----|----|
@@ -16,9 +60,13 @@ Different branches of GPTQ-for-LLaMa are available:
 
 Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.
 
-## Installation
+### Installation using precompiled wheels
 
-### Step 0: install nvcc
+https://github.com/jllllll/GPTQ-for-LLaMa-Wheels
+
+### Manual installation
+
+#### Step 0: install nvcc
 
 ```
 conda activate textgen
@@ -29,7 +77,7 @@ The command above takes some 10 minutes to run and shows no progress bar or upda
 
 See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571
 
-### Step 1: install GPTQ-for-LLaMa
+#### Step 1: install GPTQ-for-LLaMa
 
 Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it:
 
@@ -64,14 +112,14 @@ git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton
 
 https://github.com/qwopqwop200/GPTQ-for-LLaMa
 
-### Step 2: get the pre-converted weights
+#### Step 2: get the pre-converted weights
 
 * Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
 * Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 
 
 ⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights).
 
-### Step 3: Start the web UI:
+#### Step 3: Start the web UI:
 
 For the models converted without `group-size`:
 
@@ -91,7 +139,7 @@ The command-line flags `--wbits` and `--groupsize` are automatically detected ba
 python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128
 ```
 
-## CPU offloading
+### CPU offloading
 
 It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU.
 
@@ -109,20 +157,13 @@ Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)
 
 You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of GPTQ, eg `--pre_layer 30 60` will load a LLaMA-30B model half onto your first GPU and half onto your second, or `--pre_layer 20 40` will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU.
 
-## Using LoRAs in 4-bit mode
+### Using LoRAs with GPTQ-for-LLaMa
 
-At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
+This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
 
-In order to use it:
+To use it:
 
-1. Make sure that your requirements are up to date:
+1. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
-
-```
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-
-2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
 
 ```
 cd text-generation-webui/repositories
@@ -131,13 +172,13 @@ git clone https://github.com/johnsmith0031/alpaca_lora_4bit
 
 ⚠️  I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0`
 
-3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
+2. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
 
 ```
 pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
 ```
 
-4. Start the UI with the `--monkey-patch` flag:
+3. Start the UI with the `--monkey-patch` flag:
 
 ```
 python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch

extensions/openai/script.py

@@ -259,7 +259,7 @@ class Handler(BaseHTTPRequestHandler):
 
                 role_formats = {
                     'user': 'user: {message}\n',
-                    'bot': 'assistant: {message}\n',
+                    'assistant': 'assistant: {message}\n',
                     'system': '{message}',
                     'context': 'You are a helpful assistant. Answer as concisely as possible.',
                     'prompt': 'assistant:',