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If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
This is just tangential, but I wouldn't call their APIs "nice", I'd be far less charitable. I spent a few hours (because that's how long it took to figure out the API, due to almost zero documentation) and wrote a nicer Python layer:
https://github.com/skorokithakis/ez-openai/
With all that money, I would have thought they'd be able to design more user-friendly APIs. Maybe they could even ask an LLM for help.
> Mistral's latest just released model is well below GPT-3 out of the box
The early information I see implies it is above. Mind you, that is mostly because GPT-3 was comparatively low: for instance its 5-shot MMLU score was 43.9%, while Llama2 70B 5-shot was 68.9%[0]. Early benchmarks[1] give Mixtral scores above Llama2 70B on MMLU (and other benchmarks), thus transitively, it seems likely to be above GPT-3.
Of course, GPT-3.5 has a 5-shot score of 70, and it is unclear yet whether Mixtral is above or below, and clearly it is below GPT-4’s 86.5. The dust needs to settle, and the official inference code needs to be released, before there is certainty on its exact strength.
[0]: https://paperswithcode.com/sota/multi-task-language-understa...
[1]: https://github.com/open-compass/MixtralKit#comparison-with-o...
