From my perspective, there's a vast divide between open source models and GPT4 at present. The lmsys leaderboard rankings are derived from users independently engaging with the LLMs and opting for the answers they find most appealing. Consequently, the rankings are influenced not only by the type of questions users ask but also by their preference for succinctness in responses. When we venture into the realm of more complex tasks, such as code generation, GPT4 unequivocally outshines all open source models.

That said, the advancements in models like Orca and the "Textbooks are all you need" paper are noteworthy (https://arxiv.org/pdf/2306.02707.pdf, https://arxiv.org/abs/2306.11644). I'm optimistic about what future smaller models could achieve.

I agree. Also, ELO rankings can artificially deflate strong performers when having too few binary comparisons. The Guanaco paper points that out, giving instead a 326-point lead to GPT-4 over Guanaco-65B across 10k orderings, based on GPT-4’s opinion, which corresponds to Guanaco winning about 13% of the time. But even that only relies on the Vicuna benchmark set.

There are huge differences. I used OpenAI’s APIs in my last book on LangChain and LlamaIndex, and GPT-3.5 and GPT-4 are good enough right now to support building applications for years (although I look forward to improvements).

I am writing a new book Safe For Humans AI in which I am constraining myself to using open models that can be run on a high end PC or a leased GPU server. Yesterday I was exploring what I could do with T5-flan-XXL, and it is useful, but not as effective tool as the closed OpenAI models. I will do the same with Orca this week.

I think a lot of people see a larger gap between open-source models and GPT-4 than is present because they test out the 7B models which fit on their machine. Models like T5-FLAN-XXL are very far below the quality one can expect from the best in open-source, and barely usable for CoT or tool use.

Especially for LangChain, I recommend using ≥33B models like Guanaco or WizardLM. Guanaco-65B honestly feels on-par with ChatGPT-3.5. (To be clear, there is a large gap with GPT-4 though.) It is a costly test, although GPTQ (for instance on exllama) help make it affordable.

I haven’t tried Orca since they haven’t released the weights yet, but it doesn’t seem like they have a 33B version.

Thanks for your advice! I am trying to write the book examples using a A6000 (48G video memory), but I may have to go higher end. The hourly lease charge on a A6000 is very inexpensive and I wanted the examples to be "almost free" to run. I will see what is required for Guanaco-65B.

> The gap with GPT-4 is not that big...

Oh it is though. I've tried several OS models and nothing comes even close to GPT-4. Turns out ClosedAI has a moat after all.

AI development is taking place at breathtaking velocity and GPT-4 isn't the end of the road. OpenAI's moat relies on them improving at a faster pace than competitors. I wouldn't take it for granted that OpenAI's lead will remain the same in a year as it is today. Very possibly they could expand their lead. Also possibly, their lead could vanish.

Individual developers’ hardware and data has limit, I don’t see the OS models breaking into 100B models anytime soon

GPT-4 came roughly 12 months after GPT-3.5, and I'd say the competition is roughly approaching 3.5 now, so I'm hoping they will reach GPT-4 level early 2024. Not that far off!

Wish there was a bookmark/remind-me function so I could come back in 8 months' time and see how close I was.