refact VS ollama

With over 1.3k stars on GitHub, more than 40k downloads and installs on both VS Code and JetBrains IDEs, and more than 50 positive reviews, it is worth saying that Refact is part of the best product in the AI coding assistant market.

On vscode i sometimes use continue.dev and refact.ai just for fun and they are great!

Refact was made for this: https://github.com/smallcloudai/refact

You should check-out [refact.ai](https://github.com/smallcloudai/refact). It has both autocomplete and chat. It's in active development, with lots of new features coming soon (context search, fine-tuning for larger models, etc)

I don’t recommend that, since that uses the cloud for the actual inference by default (and they provide no guidance for changing that).

I don’t consider cloud inference to count as getting it working “locally” as requested by the comment above yours.

Refact works nicely and works locally, but the challenge with any new model is making it be supported by the existing software: https://github.com/smallcloudai/refact/

Previously, we used Astro for our refact.ai website and wanted to stay within the Astro ecosystem for the documentation.

Refact LLM can be easily integrated into existing developers workflows with an open-source docker container and VS Code and JetBrains plugins. With Refact's intuitive user interface, developers can utilize the model easily for a variety of coding tasks. Finetune is available in the self-hosting (docker) and Enterprise versions, making suggestions more relevant for your private codebase.

# install the Ollama curl -fsSL https://ollama.com/install.sh | sh # get the llama3 model ollama pull llama2 # install the MLFlow pip install mlflow

Ollama for running LLMs locally

curl -fsSL https://ollama.com/install.sh | sh

Streaming is not a problem (it's just a simple flag: https://github.com/wiktor-k/llama-chat/blob/main/index.ts#L2...) but I've never used voice input.

The examples show image input though: https://github.com/ollama/ollama/blob/main/docs/api.md#reque...

Maybe you can file an issue here: https://github.com/ollama/ollama/issues

I’m a huge fan of open source models, especially the newly release Llama 3. Because of the performance of both the large 70B Llama 3 model as well as the smaller and self-host-able 8B Llama 3, I’ve actually cancelled my ChatGPT subscription in favor of Open WebUI, a self-hostable ChatGPT-like UI that allows you to use Ollama and other AI providers while keeping your chat history, prompts, and other data locally on any computer you control.

Ollama for running LLMs locally

There are various other interesting options to set, but for those, I will direct you to the link to the documentation. During the OS Day, I had the chance to experiment a bit with the models offered by Ollama; in fact, if you need some inspiration, I invite you to check out the YouTube channel of Shroedinger Hat where you can find the videos of the individual talks, also organized in a single playlist; you will find more than one showing the use of Ollama for various projects and in various ways 😁

That’s where Ollama comes in! Ollama is a free and open-source application that allows you to run various large language models, including Llama 3, on your own computer, even with limited resources. Ollama takes advantage of the performance gains of llama.cpp, an open source library designed to allow you to run LLMs locally with relatively low hardware requirements. It also includes a sort of package manager, allowing you to download and use LLMs quickly and effectively with just a single command.