PrivateGPT4Linux VS llm

I found things like this dataset and LocalAI and I followed the article to get PrivateGPT and the GPT4ALL groovy.bin but I'm completely lost and it feels like the more I research the internet or ask BingAI for answers, the more questions I get instead. At this stage I don't know what goes where, if there's a difference between source documents and datasets, should I run this from my 2tb SSD? Should I have the data on my 8tb HDD? Will all this even work on my PC?

No, as the same as the last question, It does not have access to anything except the model data itself. However, there are some approaches that can let LLMs have access LOCAL documents, which means if you can have a program that extracts data from the database into a local folder which contains TEXT files. This could also work for 2(I didn't mention it in 2 because online datas are REALLY big. It would take the model hours to give an answer. If the database is not large then there might be a shot. Check https://github.com/imartinez/privateGPT(Must be GPT4all compatible models sadly).

I've been doing exactly this with an open source repository called PrivateGPT imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks (github.com)

Not really, you will probably need to change the data location and the LLM provider in the example code to get it running. But you don't have to implement that yourself there are a couple projects that already do that like privateGPT. I use it for searching datasheets, got it up an running in a few hours and I'm pretty happy with it so far.

PrivateGPT can do that.

We're open-sourcing a project that lets you build simple automations/agent workflows that use LLMs for different tasks. Kinda like Zapier or IFTTT but focused on using natural language to accomplish your tasks.It's super early but we'd love to start getting feedback to steer it in the right direction. It currently supports OpenAI and local models through llm.

> Tensorflow is a C++ framework that has Python bindings and a Python library, but when the models are served they are running on C++

Sure, and it's only a simple 20 step process that involves building Tensorflow from source. Yeay!

https://medium.com/@hamedmp/exporting-trained-tensorflow-mod...

Let me see what the process for compiling a LLM written in Rust is....

https://github.com/rustformers/llm

    cargo install llm-cli

Floneum is a graph editor for local AI workflows. It uses llm to run large language models locally, egui, and dioxus for the frontend, and wasmtime for the plugin system. If you are interested in the project, consider joining the discord, or building a plugin for Floneum in rust using WASI

Run inference for Large Language Models on CPU, with Rust (https://github.com/rustformers/llm)

On the rustformers github page I see that one of the commands to generate the answer is llm llama infer -m ggml-gpt4all-j-v1.3-groovy.bin -p "Rust is a cool programming language because", my basic idea for now is to change the Tauri app to let it do -p prompt, which receives from my code through the link or through a shared variable (if I don't use the link and start different times your app)

wonnx has done some fantastic work in this regard, so that's where we plan to start once we get there. In terms of general discussion of alternate backends, see this issue.