2024-05-29 18:45 - Cuda check 2024-05-29 18:45 - True 2024-05-29 18:45 - 1 2024-05-29 18:45 - Configue Model and tokenizer 2024-05-29 18:45 - Memory usage in 0.00 GB 2024-05-29 18:45 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 18:46 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 18:49 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 18:49 - Setup PEFT 2024-05-29 18:49 - Setup optimizer 2024-05-29 18:49 - Start training 2024-05-29 18:57 - Cuda check 2024-05-29 18:57 - True 2024-05-29 18:57 - 1 2024-05-29 18:57 - Configue Model and tokenizer 2024-05-29 18:57 - Memory usage in 25.17 GB 2024-05-29 18:57 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 18:57 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 18:57 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 18:57 - Setup PEFT 2024-05-29 18:57 - Setup optimizer 2024-05-29 18:57 - Start training 2024-05-29 19:04 - Cuda check 2024-05-29 19:04 - True 2024-05-29 19:04 - 1 2024-05-29 19:04 - Configue Model and tokenizer 2024-05-29 19:04 - Memory usage in 25.17 GB 2024-05-29 19:04 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 19:04 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 19:04 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 19:04 - Setup PEFT 2024-05-29 19:04 - Setup optimizer 2024-05-29 19:04 - Start training 2024-05-29 19:10 - Cuda check 2024-05-29 19:10 - True 2024-05-29 19:10 - 1 2024-05-29 19:10 - Configue Model and tokenizer 2024-05-29 19:10 - Memory usage in 25.17 GB 2024-05-29 19:10 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 19:10 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 19:10 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 19:10 - Setup PEFT 2024-05-29 19:10 - Setup optimizer 2024-05-29 19:10 - Start training 2024-05-29 19:16 - Cuda check 2024-05-29 19:16 - True 2024-05-29 19:16 - 1 2024-05-29 19:16 - Configue Model and tokenizer 2024-05-29 19:16 - Memory usage in 25.17 GB 2024-05-29 19:16 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 19:16 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 19:16 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 19:16 - Setup PEFT 2024-05-29 19:16 - Setup optimizer 2024-05-29 19:16 - Start training 2024-05-29 19:22 - Cuda check 2024-05-29 19:22 - True 2024-05-29 19:22 - 1 2024-05-29 19:22 - Configue Model and tokenizer 2024-05-29 19:22 - Memory usage in 25.17 GB 2024-05-29 19:22 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 19:22 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 19:22 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 19:22 - Setup PEFT 2024-05-29 19:22 - Setup optimizer 2024-05-29 19:22 - Start training 2024-05-29 19:29 - Cuda check 2024-05-29 19:29 - True 2024-05-29 19:29 - 1 2024-05-29 19:29 - Configue Model and tokenizer 2024-05-29 19:29 - Memory usage in 25.17 GB 2024-05-29 19:29 - Dataset loaded successfully: train-Jingmei/Pandemic_Wiki test -Jingmei/Pandemic_WHO 2024-05-29 19:29 - Tokenize data: DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 2152 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 8264 }) }) 2024-05-29 19:29 - Split data into chunks:DatasetDict({ train: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 24863 }) test: Dataset({ features: ['input_ids', 'attention_mask'], num_rows: 198964 }) }) 2024-05-29 19:29 - Setup PEFT 2024-05-29 19:29 - Setup optimizer 2024-05-29 19:29 - Start training