Meta releases ‘Code Llama 70B’, an open-source behemoth to rival private AI development
Meta releases ‘Code Llama 70B’, an open-source behemoth to rival private AI development
Code Llama 70B is a new and improved version of Meta AI’s code generation model that can write code in various programming languages from natural language prompts or existing code snippets.
You're viewing a single thread.
Anyone using the code specific models, -- how are you prompting them? Are you using any integration into vim emacs or other truly open source and offline text editor/IDE; not electron or proton based? I've compiled VS code before, but it is basically useless in that form, and the binary version sends network traffic like crazy.
I've downloaded the 13B codellama from huggingface, passed it my NVIDIA 2070 via cuda, and have interfaced either through the terminal or lmstudio.
Usually my prompts include the specific code block and a wordy explanation about what I'm trying to do.
It's okay, but it's not as accurate as chatgpt, and tends to repeat itself a lot more.
For editor integration, i just opted for codeium in neovim. It's a pretty good alternative to copilot imho.
Hugging face have an llm plug-in for code completion in neovim btw!
Oh nice! Got a link for anyone that comes across this? Save me and others a search plz?
EDIT: NM. Got it. Gonna give it a try later.
If you use ollama you can try to use the fork that I am using. This is my config to make it work: https://github.com/Amzd/nvim.config/blob/main/lua/plugins/llm.lua
Nice. Thanks. I'll save this post in case I use ollama in the future. Right now I use a codellama model and a mythomax model, but am not running them via a localhost server, just outputted in the terminal or LMStudio.
This looks interesting though. Thanks!
Why use it though if it's not as good, and repeats itself.
Because it doesn't call out to the internet. I even put lmstudio behind firejail to prevent it from doing so. Thusly any code I feed it (albeit pretty trivial code) doesn't add to chatgpt's overarching data set.
It still can produce usable results. It's just not as consistent. Whenever it gets into a repetitive loop, I just restart it, resetting the initial context, which generally prevents it from repeating itself, at least initially. To be fair, I've also experienced this with chatgpt, just not as often.
TLDR; It's more private and still useful.