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Andrej Karpathy - Cannibalizing LLMs - Good - Google - Hugging Face - Llama 2 - LLM - LM Fine-tuning - My M2Max mac - NLP@Meta - OpenAI - Open Source - Open-source LLM - Text to SQL - Tutorial - Tweet -

AI@Meta ; Open-source LLM AND Unsupervised machine learning

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Llama 2

10 Documents

Getting started with Llama 2 - AI at Meta

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2023-10-28 About

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

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2023-09-01 About

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

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2023-08-18 About

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

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2023-08-18 About

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

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2023-08-02 About

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 About

Llama 2 is here - get it on Hugging Face

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2023-07-19 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

Llama 2 - Meta AI

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Llama 2

2023-07-19 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