for step, batch in enumerate(train_dataloader): with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() if accelerator.sync_gradients and accelerator.is_main_process: tensorboard_writer....
Algorithmic CI ML CI Hardware CI Paper Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication Abstract The recent Maddness method approximates Matrix Multiplication (MatMul) without the need for multiplication by using a hash-based version ...
Here are some results of train and val losses (You can visualize this in TensorBoard in VS code) Data Handling We basically collected this data from open source translated English books of this scriptures. After copy and pasting of this books, we cleaned those books with some technics. Those...
Here are some results of train and val losses (You can visualize this in TensorBoard in VS code) Data Handling We basically collected this data from open source translated English books of this scriptures. After copy and pasting of this books, we cleaned those books with some technics. Those...