noiseinvacuum
Joined 3 years ago
Joined 3 years ago
This is going to be a very interesting platform battle to watch.
https://www.tomsguide.com/news/apple-glasses-reportedly-dela...
It's helpful to also look at what do the developers and companies (everyone outside of top 5/10 big tech companies) get out of this. They get open access to weights of SOTA LLM models that take billions of dollars to train and 10s of billions a year to run the AI labs that make these. They get the freedom to fine tune them, to distill them, and to host them on their own hardware in whatever way works best for their products and services.
More important is the products that Meta will be able to make if the industry standardizes on Llama. They would have the front seat in not just with access the latest unreleased models but also settings the direction of progress and next gen LLM optimizes for. If you're Twitter or Snap or TikTok or compete with Meta on the product then good luck in trying to keep up.
In this case both local storage and cloud storage are provided by same company so that distinction doesn’t matter but this storage location agnostic coverage of GDPR comes in handy on Android.
* Kept for no longer than necessary * Deleted upon request or when no longer needed
Both of these conditions are met when someone "deletes" a picture from their device.
This bug basically proved that Apple is non compliant with this and there's no way that EU is going to ignore this big of a violation. If found guilty then they can fine Apple 20 million euros or 4% of global turnover(revenue) which is > $4 billion.
I am sure this is also a GDPR violation and EU is probably already looking into it.
From the huggingface page:
Model Details
We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augumented generation (RAG). Llama3-ChatQA-1.5 is built using the training recipe from ChatQA (1.0), and it is built on top of Llama-3 base model. Additionally, we incorporate more conversational QA data to enhance its tabular and arithmatic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using Megatron-LM, we converted the checkpoints to Hugging Face format. Other Resources
https://www.reddit.com/r/MachineLearning/s/GK57eB2qiz
Some noteworthy quotes that signal the thought process at Meta FAIR and more broadly
* We’re just playing a different game on the infrastructure than companies like Google or Microsoft or Amazon
* We would aspire to and hope to make even more open than that. So, we’ll need to figure out a way to do that.
* ...lead us to do more work in terms of open sourcing, some of the lower level models and tools
* Open sourcing low level tools make the way we run all this infrastructure more efficient over time.
* On PyTorch: It’s generally been very valuable for us to provide that because now all of the best developers across the industry are using tools that we’re also using internally.
* I would expect us to be pushing and helping to build out an open ecosystem.