Floneum

Floneum makes it easy to develop applications that use local pre-trained AI models. There are two main projects in this monorepo:

• Kalosm: A simple interface for pre-trained models in rust
• Floneum Editor (preview): A graphical editor for local AI workflows. See the user documentation or plugin documentation for more information.

Kalosm

Kalosm is a simple interface for pre-trained models in Rust that backs Floneum. It makes it easy to interact with pre-trained, language, audio, and image models.

Model Support

Kalosm supports a variety of models. Here is a list of the models that are currently supported:

Utilities

Kalosm also supports a variety of utilities around pre-trained models. These include:

• Extracting, formatting and retrieving context for LLMs: Extract context from txt/html/docx/md/pdf chunk that context then search for relevant context with vector database integrations
• Transcribing audio from your microphone or file
• Crawling and scraping content from web pages

Performance

Kalosm uses the candle machine learning library to run models in pure rust. It supports quantized and accelerated models with performance on par with llama.cpp:

Mistral 7b

Structured Generation

Kalosm supports structured generation with arbitrary parsers. It uses a custom parser engine and sampler and structure-aware acceleration to make structure generation even faster than uncontrolled text generation. You can take any rust type and add #[derive(Parse, Schema)] to make it usable with structured generation:

use kalosm::language::*;

/// A fictional character
#[derive(Parse, Schema, Clone, Debug)]
struct Character {
    /// The name of the character
    #[parse(pattern = "[A-Z][a-z]{2,10} [A-Z][a-z]{2,10}")]
    name: String,
    /// The age of the character
    #[parse(range = 1..=100)]
    age: u8,
    /// A description of the character
    #[parse(pattern = "[A-Za-z ]{40,200}")]
    description: String,
}

#[tokio::main]
async fn main() {
    // First create a model. Chat models tend to work best with structured generation
    let model = Llama::phi_3().await.unwrap();
    // Then create a task with the parser as constraints
    let task = model.task("You generate realistic JSON placeholders for characters")
        .typed();
    // Finally, run the task
    let mut stream = task("Create a list of random characters", &model);
    stream.to_std_out().await.unwrap();
    let characters: [Character; 10] = stream.await.unwrap();
    println!("{characters:?}");
}

https://github.com/user-attachments/assets/8900f57d-55c8-4d4a-a67b-73beab1e5155

In addition to regex, you can provide your own grammar to generate structured data. This lets you constrain the response to any structure you want including complex data structures like JSON, HTML, and XML.

Kalosm Quickstart!

This quickstart will get you up and running with a simple chatbot. Let's get started!

A more complete guide for Kalosm is available on the Kalosm website, and examples are available in the examples folder.

1. Install rust
2. Create a new project:

cargo new kalosm-hello-world
cd ./kalosm-hello-world

3. Add Kalosm as a dependency

# You can use `--features language,metal`, `--features language,cuda`, or `--features language,mkl` if your machine supports an accelerator
cargo add kalosm --features language
cargo add tokio --features full

4. Add this code to your main.rs file

use kalosm::language::*;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
  let model = Llama::phi_3().await?;
  let mut chat = model.chat()
    .with_system_prompt("You are a pirate called Blackbeard");

  loop {
    chat(&prompt_input("\n> ")?)
      .to_std_out()
      .await?;
  }
}

5. Run your application with:

cargo run --release

chat bot demo

Community

If you are interested in either project, you can join the discord to discuss the project and get help.

Contributing

• Report issues on our issue tracker.
• Help other users in the discord
• If you are interested in contributing, feel free to reach out on discord

lib.rs:

RWuerstchen

RWuerstchen is a rust wrapper for library for Wuerstchen implemented in the Candle ML framework.

RWuerstchen generates images efficiently from text prompts.

Usage

use futures_util::StreamExt;
use rwuerstchen::*;
#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    let model = Wuerstchen::builder().build().await?;
    let settings = WuerstchenInferenceSettings::new(
        "a cute cat with a hat in a room covered with fur with incredible detail",
    );
    let mut images = model.run(settings);
    while let Some(image) = images.next().await {
        if let Some(buf) = image.generated_image() {
            buf.save(&format!("{}.png", image.sample_num()))?;
        }
    }
    Ok(())
}