GPTQ-for-LLaMa VS private-gpt

git clone https://github.com/oobabooga/GPTQ-for-LLaMa

If you're using the model directly from ehartford, that one hasn't been quantized. Try using the GPTQ quantized version here, and use this fork of GPTQ-for-LLaMa. Load in 4-bit with --wbits 4

This worked for me on Ubuntu. If you want to use the CUDA branch instead of triton, do the same steps except clone this GPTQ-for-LLaMa fork and run python setup_cuda.py install

If you don't have triton and you use AutoGPTQ you're gonna notice a huge slow down compared to the old GPTQ-for-LLaMA cuda branch. For me AutoGPTQ gives me a whopping 1 token per second compared to the old GPTQ that gives me a decent 9 tokens per second.. both times I used a same sized model. (I think the slowdown is due to AutoGPTQ using the newer cuda branch which is much slower than the old one)

Are you using a later version of GPTQ-for-LLaMa? If so, go to ooba's CUDA fork (https://github.com/oobabooga/GPTQ-for-LLaMa). That's what I made it in and it definitely works with that. And that's what's included in the one-click-installers.

This usually happens when using models that conflict with your GPTQ installation. You should be using this fork: https://github.com/oobabooga/GPTQ-for-LLaMa. If you did the manual installation wrong, use the one click installer instead.

cd text-generation-webui # wherever you have it installed mkdir -p repositories cd repositories git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda GPTQ-for-LLaMa cd GPTQ-for-LLaMa python setup_cuda install

I'm on GPTQ for LLaMA folder under repositories says it's pointed at https://github.com/oobabooga/GPTQ-for-LLaMa.git. But I've run through the instructions and also applied the monkey patch to train and apply 4 bit lora which may come into play. No idea.

git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda

PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.

https://github.com/zylon-ai/private-gpt

Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion

Run https://github.com/imartinez/privateGPT

Then

make ingest /path/to/folder/with/files

Then chat to the LLM.

Done.

Docs: https://docs.privategpt.dev/overview/welcome/quickstart

PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.

I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].

The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.

[0] https://gpt4all.io/index.html

[1] https://www.danswer.ai/

[2] https://github.com/imartinez/privateGPT

https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...

https://github.com/imartinez/privateGPT

I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.

This resulted in Python package I call OnPrem.LLM.

In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/

Enjoy!