llm VS lora

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.

Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora

Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.

No, it's a deep learning thing

Low Rank Adaptation of Large Language Models.

Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.

Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.

Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685

Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...

Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685