> For instance, LLaMA-13B outperforms GPT-3 on most bench- marks, despite being 10× smaller. We believe that this model will help democratize the access and study of LLMs, since it can be run on a single GPU.
Without the fine tuning RLHF phase to make it like instructgpt I'm assuming it won't be as good as ChatGPT, is that right?
How hard would it be to fine tune the 65B model on commodity hardware?
Found answer here:
> Out-of-scope use cases LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
https://github.com/facebookresearch/llama/blob/main/MODEL_CA...
They kept the details closely guarded and only hinted at how they did RLHF and transitioned the architecture to self supervised learning.