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4bit/WizardLM-7B-uncensored-GPTQ
WizardLM-7B-uncensored-GPTQ is a text generation model from 4bit. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
These files are GPTQ 4bit model files for Eric Hartford's 'uncensored' version of WizardLM.
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From the Hugging Face model README
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.
Open the text-generation-webui UI as normal.
TheBloke/WizardLM-7B-uncensored-GPTQ.WizardLM-7B-uncensored-GPTQ.GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = LlamaCompatible 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
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
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
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.
