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Favoris ; Data mining ; OpenAI AND NLP@Meta

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LLaMA

22 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 A propos

Getting started with Llama 2 - AI at Meta

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2023-10-28 A propos

Philipp Schmid sur X : “YaRN” allows you to scale LLMs like llama 2 to over 100k context!...

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2023-09-01 A propos

Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications | Anyscale

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2023-08-18 A propos

Easily Finetune Llama 2 for Your Text-to-SQL Applications | by Jerry Liu | Aug, 2023

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2023-08-18 A propos

Andrej Karpathy sur Twitter : "How is LLaMa.cpp possible?"

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2023-08-16 A propos

Simon Willison sur Twitter : "Run Llama 2 on your own Mac using LLM and Homebrew..."

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2023-08-02 A propos

Andrej Karpathy sur Twitter : "My fun weekend hack: llama2.c Lets you train a baby Llama 2 model in PyTorch, then inference it with one 500-line file with no dependencies, in pure C."

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2023-07-24 A propos

Llama 2 is here - get it on Hugging Face

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2023-07-19 A propos

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 A propos

Llama 2 - Meta AI

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Llama 2

2023-07-19 A propos

ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++

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2023-06-04 A propos

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

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2023-05-26 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

Jeremy Howard sur Twitter : "There's a lot of folks under the misunderstanding that it's now possible to run a 30B param LLM in <6GB, based on this GitHub discussion. This is not the case..." (thread)

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2023-04-03 A propos

Using ChatGPT Plugins with LLaMA

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2023-03-27 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

Guillaume Lample sur Twitter : "Today we release LLaMA, 4 foundation models ranging from 7B to 65B parameters..."

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2023-02-25 A propos

Akari Asai sur Twitter : "Can we solely rely on LLMs’ memories (eg replace search w ChatGPT)? Probably not... Our analysis shows how retrieval is complementary to LLMs’ parametric knowledge..."

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2022-12-21 A propos