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WizardLM: An Instruction-following LLM Using Evol-Instruct

These files are the result of merging the delta weights with the original Llama7B model.

The code for merging is provided in the WizardLM official Github repo.

WizardLM-7B 4bit GPTQ

This repo contains 4bit GPTQ models for GPU inference, quantised using GPTQ-for-LLaMa.

Other repositories available

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.

Instructions on using GPTQ 4bit files in text-generation-webui are here.

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.


For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

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.


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