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WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct

These files are GPTQ 4bit model files for Eric Hartford's 'uncensored' version of WizardLM .

It is the result of quantising to 4bit using GPTQ-for-LLaMa .

Eric did a fresh 7B training using the WizardLM method, on a dataset edited to remove all the "I'm sorry.." type ChatGPT responses .

Other repositories available

How to easily download and use this model in text-generation-webui

Open the text-generation-webui UI as normal.

  • Click the Model tab .
  • Under Download custom model or LoRA , enter TheBloke/WizardLM-7B-uncensored-GPTQ .
  • Click Download .
  • Wait until it says it's finished downloading.
  • Click the Refresh icon next to Model in the top left.
  • In the Model drop-down : choose the model you just downloaded, WizardLM-7B-uncensored-GPTQ .
  • If you see an error in the bottom right, ignore it - it's temporary.
  • Fill out the GPTQ parameters on the right: Bits = 4 , Groupsize = 128 , model_type = Llama
  • Click Save settings for this model in the top right.
  • Click Reload the Model in the top right.
  • Once it says it's loaded, click the Text Generation tab and enter a prompt!
  • Provided files

    Compatible file - WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors

    In the main branch - the default one - you will find WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors

    This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility

    It was created without the --act-order parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.

    • wizard-vicuna-13B-GPTQ-4bit.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:
        python llama.py models/ehartford_WizardLM-7B-Uncensored c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/eric-gptq/WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors
        

    Discord

    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!

    Eric's original model card

    This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.

    Shout out to the open source AI/ML community, and everyone who helped me out, including Rohan, TheBloke, and Caseus

    WizardLM's original model card

    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.