Chital
Native macOS app for using local LLMs
What it does
A native macOS app for chatting with Ollama models Features - Low memory usage and fast app launch times - Support for multiple chat threads - Switch between different models - Markdown support - Automatic chat thread title summarization
Does a similar job
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- IMI made an app to use local AI as daily driver2024 · recurse.chat · ▲637
Hi Hackers, Excited to share a macOS app I've been working on: https://recurse.chat/ for chatting with local AI. While it's amazing that you can run AI models locally quite easily these days (through llama.cpp / llamafile / ollama / llm CLI etc.), I missed feature complete chat interfaces. Tools like LMStudio are super powerful, but there's a learning curve to it. I'd like to hit a middleground of simplicity and customizability for advanced users. Here's what separates RecurseChat out from similar apps: - UX designed for you to use local AI as a daily driver.…
- NMNative macOS app for chatting with Ollama models2024 · github.com · ▲5
Hello everyone, I've built a simple macOS app for chatting with models downloaded by Ollama - https://github.com/sheshbabu/Chital It's written in Swift, consumes less memory and loads fast. It has these features: * Support for multiple chat threads * Switch between different models * Markdown support * Automatic chat thread title summarization It's my first time working with Swift and Xcode, and it has been an interesting journey. The performance of the application is a big plus when building native apps, but I wonder if I'll be able to add features like document…
- OROllama – Run LLMs on your Mac2023 · github.com · ▲284
Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…
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