#python#large_language_models#machine_learning_systems#natural_language_processing
Flash Linear Attention (FLA) is a fast, memory-efficient library for advanced linear attention models used in transformers, written in PyTorch and Triton, and compatible with NVIDIA, AMD, and Intel GPUs. It offers many state-of-the-art linear attention models and fused modules that speed up training and reduce memory use. You can easily replace standard attention layers in your models with FLA’s efficient versions, improving training and inference speed, especially for long sequences. FLA supports hybrid models mixing linear and standard attention, and integrates with Hugging Face Transformers for easy use and evaluation. This helps you train and run large language models faster and with less memory, making your AI projects more efficient and scalable.
https://github.com/fla-org/flash-linear-attention
It might look a little confusing at first, but pay attention.
On this chart we have DXY, GOLD and BTC.
DXY, which is a representative chart of the USD, has an inverse relationship with GOLD and BTC.
This is because when the dollar is strong, big money moves into high interest bonds that are safe.
When the dollar weakens, big money moves into safe havens and riskier assets.
What we can firstly see on the chart is that every time DXY drops, GOLD almost instantly moves higher.
Secondly, we can see that BTC also moves higher, but it lags behind GOLD.
But that’s not the most interesting thing…
The most interesting thing here is that every single time DXY has entered a down trend, BTC has began its move between 133 days and 140 days later.
This time, that gives us an end of May date as to when BTC will begin its next true move higher.
It’s just facts, right there for you to see.
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