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
- 4bit GPTQ models for GPU inference
- 4bit and 5bit GGML models for CPU inference
- Eric's unquantised model in HF format
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
- 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,
- If you see an error in the bottom right, ignore it - it's temporary.
- Fill out the
GPTQ parameterson 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!
Compatible file - WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors
main branch - the default one - you will find
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.
- 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
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
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.
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Inference API has been turned off for this model.