like batch size, number of epochs, learning rate (LR), etc., which you can play with. Runningfinetune.pyis not compulsory. Otherwise, the executor file reads the foundation model and weights fromtloen/alpaca-lora-7b.
n_epochs 整數 要為其定型模型的 Epoch 數目。 Epoch 是指透過定型數據集的一個完整迴圈。 選取[預設值 ] 以使用微調作業的預設值,或選取 [進階 ] 以顯示和編輯超參數值。 [ 進階 ] 選項可讓您設定下列超參數: 展開表格 參數名稱描述 Epoch 數目 要用於定型模型的 Epoch 數目。 Epoch 是指透過定型數據...
output_path: The save path of the final model after merging the base model and Lora weights, note it down as it will be needed for deployment. num_epochs: Training parameter, the number of training epochs. It can be set to 1 for testing, usually set to 3~10. ...
Depending on your configuration, the fine-tuning will eventually complete. Figure 11 shows a snapshot that was taken after the fine-tuning of pre-trained Llama 3.1 8B model with LoRA, a batch size of 8, and a context length of 512, over 5 epochs using 8x NVIDIA A10 GPUs on OpenShift ...
num_epochs = 4 batch_size = 1 block_size = 1024 trainer = "sft" warmup_ratio = 0.1 weight_decay = 0.01 gradient_accumulation = 4 use_fp16 = True use_peft = True use_int4 = True lora_r = 16 lora_alpha = 32 lora_dropout = 0.045 ...
Is the pre trained LoRA weight in the'./lora-alpaca'path this file? https://huggingface.co/tloen/alpaca-lora-7b the link is the LoRA model trained by the repo owner, he made it public so we don't need to run it again, the lora-alpaca folder will be the path where your LoRA fin...
As the Llama 2 model is fine-tuned over more epochs, it continues to improve its accuracy on the SQuAD v2 task, up until about 8 epochs. It also tends to adhere more strictly to the output format, to the point of not returning an explanation in most cases (although it is still possib...
Number of epochs The number of training rounds. Increase this number to increase training cycles. Learning rate How big a step is it to update the model. Trigger keyword The token associated with your subject. Lora name The name of your LoRA file. In AUTOMATIC1111, It looks like<lora:Andy...
In this specific scenario, we start by defining the destination where the trained model will be stored using theoutput_dirattribute before defining additional hyperparameters, such as the optimization method, thelearning rate, thenumber of epochs, and more. ...
n_epochs 整數 要為其定型模型的 Epoch 數目。 Epoch 是指透過定型數據集的一個完整迴圈。 選取[預設值 ] 以使用微調作業的預設值,或選取 [進階 ] 以顯示和編輯超參數值。 [ 進階 ] 選項可讓您設定下列超參數: 展開資料表 參數名稱描述 Epoch 數目 要用於定型模型的 Epoch ...