---
license: other
inference: false
---

# 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)

## PERFORMANCE ISSUES

For reasons I can't yet understand, there are performance problems with these 4bit GPTQs that I have not experienced with any other GPTQ 7B or 13B models.

I have re-made the GPTQs several times, trying various versions of GPTQ-for-LLaMa code. But I currently can't resolve it.

Using the act-order.safetensors file on Triton code performs acceptably for me, testing on a 4090 - eg 10-13 tokens/s.  But the no-act-order.safetensor file, tested on the older CUDA oobabooga GPTQ-for-LLaMa code, returns only 4 tokens/s.

I will keep investigating and trying to work out what's happening here. But for the moment, if you're not able to use Triton GPTQ-for-LLaMa, you may want to try another 7B GPTQ model.

## GIBBERISH OUTPUT IN `text-generation-webui`?

Please read the Provided Files section below. You should use `wizardLM-7B-GPTQ-4bit-128g.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.no-act-order.safetensors`.

## Provided files

Two files are provided. **The second file will not work unless you use a recent version of GPTQ-for-LLaMa**

Specifically, the second 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.

Unless you are able to use the latest GPTQ-for-LLaMa code, please use `wizardLM-7B-GPTQ-4bit-128g.no-act-order.safetensors`.
 
* `wizardLM-7B-GPTQ-4bit-128g.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
  * Works on Windows
  * 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.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 run in `text-generation-webui`

File `wizardLM-7B-GPTQ-4bit-128g.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.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui.

# 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.

![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_running.png)