Semanlink - Cannibalizing LLMs

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Cannibalizing LLMs

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4 Documents (Liste longue)

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 A propos

Aran Komatsuzaki sur Twitter : "The False Promise of Imitating Proprietary LLMs..."

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2023-06-03 A propos

@yoavgo sur Twitter : "searching by description can be very useful, but current embedding models will give you texts that are *similar* to the description, rather than texts that *adhere to it*..."

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2023-05-24 A propos

Google "We Have No Moat, And Neither Does OpenAI"

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> low-cost public involvement was enabled
by a vastly cheaper mechanism for fine tuning called low
rank adaptation ()[LoRA](tag:lora)

> **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 A propos

Propriétés

sl:creationDate : 2023-05-24
sl:creationTime : 2023-05-24T01:19:16Z
rdf:type : sl:Tag
skos:prefLabel : Cannibalizing LLMs