Semanlink - Favoris ; Machine learning: techniques AND Cannibalizing LLMs

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Alpaca - Arxiv Doc - Blog - Cannibalizing LLMs - ChatGPT - Chris Olah - Dataset quality - Fine-tuning - GitHub Pages - GitHub project - Good - Google - Google AI Blog - Instruction tuning - Jeremy Howard - Language Model - LLaMA - LLM - Machine learning - MongoDB - Neural networks - NLP@Google - NLP@Meta - OpenAI - Open Source - Ranking (information retrieval) - Searching by description - Tutorial - Tweet - Yoav Goldberg -

Favoris ; Machine learning: techniques AND Cannibalizing LLMs

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Alpaca

20 Documents

Improving Search Ranking with Few-Shot Prompting of LLMs | Vespa Blog

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2023-07-07 A propos

[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

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

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2023-05-26 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

Build a ChatGPT with your Private Data using LlamaIndex and MongoDB | by Jerry Liu | LlamaIndex Blog | May, 2023 | Medium

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2023-05-21 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

Daniel Vila Suero sur Twitter : "Data quality is key for LLMs, but we're building Open Source LLMs with data of "unknown" quality... Introducing Alpaca GarbageCollector..."

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2023-04-05 A propos

Alpaca Finetuning of Llama on a 24G Consumer GPU

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2023-03-27 A propos

tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware

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2023-03-22 A propos

LLM Zoo at Home: LLaMA & Alpaca | bergis universe of software, hardware and ideas

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2023-03-20 A propos

Guiding Frozen Language Models with Learned Soft Prompts – Google AI Blog

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2023-02-14 A propos

The Flan Collection: Advancing open source methods for instruction tuning – Google AI Blog

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2023-02-02 A propos

Characterizing Emergent Phenomena in Large Language Models – Google AI Blog

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2023-01-26 A propos

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2021-12-17 A propos

Google AI Blog: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)

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2021-10-13 A propos

NLP Newsletter: The Annotated GPT-2, Understanding self-distillation, Haiku, GANILLA, Sparkwiki, Ethics in NLP, Torchmeta,…

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2020-02-24 A propos

Your own blog with GitHub Pages and fast_template (4 part tutorial) · fast.ai

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2020-01-21 A propos

Syncing your blog with your PC, and using your word processor · fast.ai

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2020-01-19 A propos

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2015-10-16 A propos