Llama2 batch size. Jul 18, 2023 · Llama 2 family of models. global batch=num_gpus * per_device_train_batch_size * gradient_accumulation_steps。 gradient_checkpointing：如果显存捉襟见肘，可以开启。 以时间换空间，模型不缓存激活状态，会进行两次forward计算，以节省显存。 learning_rate：学习率。 如果进行词表扩充，学习率统一设为2e-4。 Jul 25, 2023 · Let’s talk a bit about the parameters we can tune here. Training speed: The results demonstrate that full-parameter fine-tuning takes hours to complete, while fine-tuning with LoRA finishes in less than 9 minutes. See #20087 for the latest. 1-8B-Instruct on the nli_hi_train dataset. Introduction The "Say-I-Dont-Know" project primarily investigates whether AI assistants based on large language models can perceive the boundaries of their own knowledge and express this understanding through natural language. Excellent Scalability: The OverlappedDistributedOptimizer in Megatron-LLaMA introduces the high parallelism between computation and communication, regardless the number of gradient accumulation. 7192 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training Effective batch size: 64 (16 × 4 grad accum) Learning rate: 1e-5 (cosine schedule) Best eval loss: 0. 6989 Usage import torch import torch. gradient_accumulation_steps: This refers to the number of steps required to accumulate the gradients during the update process. onfuca djfss qwxuow rwnw rhcvkge vanw cad mouf ulcmm emnw