floneum VS text-embeddings-inference

It is pretty good. I am working on an application that uses SVGs as a way to draw a workflow editor UI with Dioxus: https://github.com/floneum/floneum

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## What can you build with Kalosm?

Kalosm is designed to be a flexible and powerful tool for building AI into your applications. It is a great fit for any application that uses AI models to process sensitive information where local processing is important.

Here are a few examples of applications that are built with Kalosm:

- Floneum (https://floneum.com/): A local open source workflow editor and automation tool that uses Kalosm to provide natural language processing and other AI features.

This reminds me of Floneum (https://github.com/floneum/floneum), this open-sourced tool for graph-based workflows using local LLMs.

Kalosm is a meta-framework for AI written in Rust using candle. Kalosm supports local quantized large language models like Llama, Mistral, Phi-1.5, and Zephyr. It also supports other quantized models like Wuerstchen, Segment Anything, and Whisper. In addition to local models, Kalosm supports remote models like GPT-4 and ada embeddings.

1. I would love to support additional model runners including exLlama and API based models like chat GPT. I'm less familiar with how c transformers and GPTQ compare to llama.cpp. GPTQ used to run faster because it supported GPU acceleration, but now llama.cpp supports the GPU as well so that may have changed. Feel free to open a GitHub issue to discuss this: https://github.com/floneum/floneum/issues/new/choose

2. There are a few differences:

Worth noting that this also impacts the great https://github.com/huggingface/text-embeddings-inference, which allows anyone to run state of the art embeddings with great performance.