altRoom.useGenParams : (KinkyDungeonMapIndex[MiniGameKinkyDungeonCheckpoint] || MiniGameKinkyDungeonCheckpoint)]; @@ -809,6 +812,7 @@ let KDBarricades = { lifetime: 9999, }, "BarricadeRobot": { minlevel: 4, filter: (enemy, x, y, checkpoint, type) => { return (enemy.Enemy.tags....
bst2 = xgb.Booster(params=param, model_file='xgb.model.dart') dtest2 = xgb.DMatrix('dtest.buffer') preds2 = bst2.predict(dtest2, ntree_limit=num_round) # assert they are the same assert np.sum(np.abs(preds2 - preds)) == 0#...
7 @@ export function setViewState(next: (...params: unknown[]) => unknown) { const path = state?.state?.file; if ( isMarkdownView && - fileViewTypeCache[path] && + fileViewTypeCache[path]?.viewType === FILE_VIEW_TYPE && !state.state.inlineEditor ) { const newState = { diff...
Optimizer state when using Adam: 4 bytes * 0.11B trainable params * 3 = 1.32GB Adding all of the above -> 9.51 GB ~10GB -> 1 A100 40GB GPU required 🤯. The reason for A100 40GB GPU is that the intermediate activations for long sequence lengths of 2048 and batch size of 4 fo...
Optimizer state when using Adam: 4 bytes * 0.11B trainable params * 3 = 1.32GB Adding all of the above -> 9.51 GB ~10GB -> 1 A100 40GB GPU required 🤯. The reason for A100 40GB GPU is that the intermediate activations for long sequence lengths of 2048 and batch size of ...
We'll use a single A100 40GB Colab Notebook using 🤗 PEFT (Parameter-Efficient Fine-Tuning) for all the experiments. Additionally, we'll show how to fully finetune the bigcode/starcoder (15.5B params) on a machine with 8 A100 80GB GPUs using 🤗 Accelerate's FSDP integra...