KoboldAI VS GPTQ-for-LLaMa

Pygmalion for chatting, Erebus for story writing, Wizard Vicuna Uncensored for general use including chatting, story writing or instructing, to name a few. There's lots more, but most are just the raw models that users are expected to load themselves so there isn't anything quite like ChatGPT where you just load up a single website and have all of them there. You'll either have to set up a local install using KoboldAI for running only on GPU, KoboldCPP for running only on CPU with optional splitting between CPU and GPU, or Oobabooga for CPU, GPU and splitting between CPU and GPU, if you have a powerful enough PC to run these models yourself (a PC with a 3090 can load up to a 30B model entirely in GPU, or a 65B model if you have 64GBs of RAM and a decently powerful CPU). If you don't have a powerful enough PC then you'll have to use something like KoboldAI Lite (website version) or SillyTavern to use the horde, a crowdsourced AI chatbot/LLM runner that lets people provide their hardware for others to use.

INFO | __main__:general_startup:1312 - Running on Repo: https://github.com/0cc4m/koboldai Branch: latestgptq

Occam seems to be trying or adding that into Kobold (https://github.com/0cc4m/KoboldAI/tree/4bit-plugin)

- Train your dataset - Add your created LORA to the model on the model tab - go back to the training tab perplexity and test if the perplexity is no more than about 0.3 higher than before. - If it is, lower the values in the training (epoch etc) and try again (first reload the model without the LoRA). If the perplexity is exactly the same, you can increase the values, it is trial and error. The lower the perplexity the better. - When you got a good LoRA that does not messs up the perplexity, you are done in Textgen, you can upload you LoRA to Huggingface - Merge using this gist https://gist.github.com/rondlite/c61a9eeb2904490abbc82ab6986cd5d0 (install the repo from the next step first and also `pip install peft`) Edit the gist so it has the right filenames for you project - Quantize to 4bits using https://github.com/0cc4m/GPTQ-for-LLaMa you need to change the command to ` CUDA_VISIBLE_DEVICES=0 python -m gptq.llama ./llama-hf/llama-7b c4 --wbits 4 --true-sequential --groupsize 128 --save llama7b-4bit.pt` ie remove act order and add groupsize (add order and groupsize dont work together) replace llama-hf/llama-7b with the name of your merged model It is important to use the GPTQ-for-LLaMA from the github since that is v1 and is the only one that works fast with Kobold - Test you end result for perplexity once more in Textgen. - In my case I had to do the entire process a few times over to finally get 5.109375 (which is on par with the original 13B model)

has anyone figured out how to quantize mpt models, considering that someone already did it for one of the models https://huggingface.co/OccamRazor/mpt-7b-storywriter-4bit-128g I tried using this github repository but I couldn't get it to work.

Any plans to upload a version that works with https://github.com/0cc4m/GPTQ-for-LLaMa ? I noticed most of the recent GPTQ ones you've uploaded don't load in that, which I believe is the only way to use quantized models with KoboldAI at this time. I suspect your GPTQ models were quantized in too new of a version of GPTQ. What I've noticed is that if they're quantized in that 0cc4m GPTQ they will work in the latest Ooba, but not vice-versa.

cd repos git clone https://github.com/0cc4m/GPTQ-for-LLaMa -b gptneox cd GPTQ-for-LLaMa python setup_cuda.py install cd .. cd ..

I would try with 0cc4m's fork. https://github.com/0cc4m/GPTQ-for-LLaMa

Now, where do I put the "GPTQ-for-LLaMa" folder?

1) Download + Unzip https://github.com/0cc4m/KoboldAI/tree/4bit 2) Download + Extract all files from this repo into the KoboldAI-4bit/repos folder https://github.com/0cc4m/GPTQ-for-LLaMa/tree/gptneox 3) Run install_requirements.bat as administrator 4) When asked type 1 and hit enter 5) Unzip llama-7b-hf and/or llama-13b-hf into KoboldAI-4bit/models folder 6) Run play.sh as usual to start the Kobold interface 7) You can now select the 8bit models in the webui via "AI > Load a model from its directory"