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Source channel @githubtrending · Post #15141 · Sep 13
#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
Search: #insert
@djangoproject · Post #428 · 08/30/2017, 03:40 AM
How #Django knows to #UPDATE vs. #INSERT when you call #save (), #Django follows this algorithm: If the object’s primary key attribute is set to a value that evaluates to True (i.e., a value other than None or the empty string), Django executes an UPDATE. If the object’s primary key attribute is not set or if the UPDATE didn’t update anything, Django executes an INSERT.