# WizardLM: An Instruction-following LLM Using Evol-Instruct
These files are the result of merging the [delta weights](https://huggingface.co/victor123/WizardLM) with the original Llama7B model.
The code for merging is provided in the [WizardLM official Github repo](https://github.com/nlpxucan/WizardLM).
## WizardLM-7B 4bit GPTQ
This repo contains 4bit GPTQ models for GPU inference, quantised using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
## Other repositories available
* [4bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GGML)
* [Unquantised model in HF format](https://huggingface.co/TheBloke/wizardLM-7B-HF)
## How to easily download and use this model in text-generation-webui
Open the text-generation-webui UI as normal.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/wizardLM-7B-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. Click the **Refresh** icon next to **Model** in the top left.
6. In the **Model drop-down**: choose the model you just downloaded,`wizardLM-7B-GPTQg`.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
9. Click **Save settings for this model** in the top right.
10. Click **Reload the Model** in the top right.
11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
## GIBBERISH OUTPUT IN `text-generation-webui`?
Please read the Provided Files section below. You should use `wizardLM-7B-GPTQ-4bit-128g.compat.no-act-order.safetensors` unless you are able to use the latest GPTQ-for-LLaMa code.
If you're using a text-generation-webui one click installer, you MUST use `wizardLM-7B-GPTQ-4bit-128g.compat.no-act-order.safetensors`.
## Provided files
Two files are provided. **The 'latest' file will not work unless you use a recent version of GPTQ-for-LLaMa**
Specifically, the 'latest' file uses `--act-order` for maximum quantisation quality and will not work with oobabooga's fork of GPTQ-for-LLaMa. Therefore at this time it will also not work with `text-generation-webui` one-click installers.
The 'compat' file will be used by default in text-generation-webui so you don't need to do anything special to use it. If you want to use the 'latest' file, please remove the 'cmopat' file - but only do this if you are able to use the latest GPTQ-for-LLaMa code.
* `wizardLM-7B-GPTQ-4bit-128g.compat.no-act-order.safetensors`
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with text-generation-webui one-click-installers
* Parameters: Groupsize = 128g. No act-order.
* Command used to create the GPTQ:
```
CUDA_VISIBLE_DEVICES=0 python3 llama.py wizardLM-7B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors wizardLM-7B-GPTQ-4bit-128g.no-act-order.safetensors
```
* `wizardLM-7B-GPTQ-4bit-128g.latest.act-order.safetensors`
* Only works with recent GPTQ-for-LLaMa code
* **Does not** work with text-generation-webui one-click-installers
* Parameters: Groupsize = 128g. act-order.
* Offers highest quality quantisation, but requires recent GPTQ-for-LLaMa code
* Command used to create the GPTQ:
```
CUDA_VISIBLE_DEVICES=0 python3 llama.py wizardLM-7B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors wizardLM-7B-GPTQ-4bit-128g.act-order.safetensors
```
## How to install manually in `text-generation-webui` and update GPTQ-for-LLaMa if necessary
File `wizardLM-7B-GPTQ-4bit-128g.compat.no-act-order.safetensors` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).
[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)).
The other `safetensors` model file was created using `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI.
If you want to use the act-order `safetensors` files and need to update the Triton branch of GPTQ-for-LLaMa, here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
```
# Clone text-generation-webui, if you don't already have it
git clone https://github.com/oobabooga/text-generation-webui
# Make a repositories directory
mkdir text-generation-webui/repositories
cd text-generation-webui/repositories
# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
```
Then install this model into `text-generation-webui/models` and launch the UI as follows:
```
cd text-generation-webui
python server.py --model wizardLM-7B-GPTQ --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
```
The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
If you can't update GPTQ-for-LLaMa or don't want to, you can use `wizardLM-7B-GPTQ-4bit-128g.compat.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui.
## Want to support my work?
I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.
So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.
Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.
* Patreon: coming soon! (just awaiting approval)
* Ko-Fi: https://ko-fi.com/TheBlokeAI
* Discord: https://discord.gg/UBgz4VXf
# Original model info
Overview of Evol-Instruct
Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.
