Semanlink - Favoris ; Machine learning: techniques ; Parameter-efficient fine-tuning (PEFT) AND NLP@Meta

Favoris ; Machine learning: techniques ; Parameter-efficient fine-tuning (PEFT) AND NLP@Meta

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4 Documents

Jeremy Howard sur X : "FSDP/QDoRA with Llama3 : I believe that this combination is likely to create better task-specific models than anything else at any cost."

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2024-04-23 About

clem 🤗 sur Twitter : "Llama 2 by @Meta is already integrated with @huggingface transformers, TGI, inference endpoints, PEFT and much more..."

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2023-07-19 About

How to use Alpaca-LoRA to fine-tune a model like ChatGPT

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2023-05-26 About

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 About