Semanlink - Open Source AND OpenAI

> **Part of what makes LoRA so effective is that ... it’s stackable.**
>
> By contrast, training giant models from scratch not only
throws away the pretraining, but also any iterative
improvements that have been made on top.

> LoRA updates are very cheap to produce (~$100) for the
most popular model sizes.

> Many of these projects are saving time by training on
small, highly curated datasets...
> These
datasets are built using synthetic methods (e.g. filtering
the best responses from an existing model) and
scavenging from other projects

> Directly Competing With Open Source
Is a Losing Proposition

> Paradoxically, the one clear winner in all of this is Meta.
Because the leaked model was theirs ([LLaMA](tag:llama)), they have
effectively garnered an entire planet's worth of free labor.
Since most open source innovation is happening on top of
their architecture, there is nothing stopping them from
directly incorporating it into their products.

2023-05-04 About

Harrison Chase sur Twitter : "LangChain AIPlugins: A first open source attempt at using AIPlugins (the same ones ChatGPT is using)

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2023-03-24 About

Jimmy Lin sur Twitter : "GPT-4 and its ilk are awesome for rapid prototyping and one-offs, but at the end of the day, enterprises will deploy far smaller distilled models in production. Here's my contrarian take -" / Twitter

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2023-03-21 About

Ramsri Goutham Golla sur Twitter : "The most practical open-source competitor to @OpenAI 's GPT-3 is Google's Flan-T5 Here are 5 Flan-T5 resources to try out easily, deploy, or fine-tune it! 🧵" / Twitter

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2023-02-04 About

merve sur Twitter : "Do you want to know how models like ChatGPT work? Did you know you could build your own conversational product using open-source alternatives..."

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2022-12-06 About