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Save bandwidth by using a torrent to distribute more efficiently #73

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2 changes: 1 addition & 1 deletion README.md
@@ -1,7 +1,7 @@
# LLaMA

This repository is intended as a minimal, hackable and readable example to load [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) ([arXiv](https://arxiv.org/abs/2302.13971v1)) models and run inference.
In order to download the checkpoints and tokenizer, fill this [google form](https://forms.gle/jk851eBVbX1m5TAv5)
In order to download the checkpoints and tokenizer, fill this [google form](https://forms.gle/jk851eBVbX1m5TAv5) or if you want to save our bandwidth use this BitTorrent link: "[magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA](magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA)".
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LGTM.

I'd rather style it this way instead, given that all other links are using this style.

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In order to download the checkpoints and tokenizer, fill this [google form](https://forms.gle/jk851eBVbX1m5TAv5) or if you want to save our bandwidth use this BitTorrent link: "[magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA](magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA)".
In order to download the checkpoints and tokenizer, fill this [google form](https://forms.gle/jk851eBVbX1m5TAv5) or if you want to save our bandwidth [use this BitTorrent link](magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA)".

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Objectively better like this.

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Get this person promoted to VP Product ASAP

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LGTM.

I'd rather style it this way instead, given that all other links are using this style.

Thank you!
Transmission accepted this link but not the other one!


### Setup

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buenas como hago para conseguir llama version 65b. o el torrent. porque estoy rastreando hace rato para instalar y probar encontre los pesos peor me faltan archivos de instalacion, si alguno me puede alyudar le agradezco , aunque sea por mje.privado. slot1_@hotmail.com.

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@ADANCE-STACK were you able to download it? I'm also looking for it

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Downloading the Llama 7B, taking forever...

In a conda env with pytorch / cuda available, run
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