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.
I don't like to sound like a broken clock, but all the llama models have restrictions on their use that mean they aren't open source.
And they don't provide the source... So it's neither open nor source. I get why and how Meta tries to make themselves look better. And I'm grateful for having access to such models. But I think words have meanings and journalists should do better than repeat that phrasing and help watering down the meaning of 'open source'. (Which technically doesn't mean free or without restrictions, but is often used synonymously.)
How well do the OpenLlama models perform against Llama2? AIUI the training data uses for OpenLlama is the same?
The training data for OpenLlama is called RedPajama if I'm not mistaken. And a reproduction of what Meta used to train the first LLaMA. Back then they listed the datasets in the scientific paper. Nowadays they and their competitors don't do that anymore.
OpenLlama performs about as good (slightly worse) as the first official LLaMA. And both perform worse than Llama2. It's not day and night, but i think a noticeable improvement. And Llama2 has twice the context length which is a huge improvement for some use-cases.
If you're looking for models with a different license, there are some more. Mistral is Apache 2.0 and there are several more with permissive licenses.
If you're looking for info on what datasets the big players use, forget it (my opinion). The companies are all involved in legal battles over copyright and have stopped publishing what they use. Many (except for Meta) have kept it a (trade) secret from the beginning and never shared such information. It's unscientific because it doesn't allow for repeatability. But AI is expensive and everyone is currently trying to get obscenely rich with it or strives for world domination.
But datasets are available, like the RedPajama one, several other collections for various purposes... Lots of datasets for fine-tuning and a whole community around that. Just for the base/foundation models, we don't have access to a current state of the art dataset for that.
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.
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