nlp_language_modeling/merge_lora_weights/merge.py\trainer.accelerator=gpu\# Use 'cpu' if the model cannot fit in memorytensor_model_parallel_size=${TP_SIZE}\pipeline_model_parallel_size=1\gpt_model_file=${MODEL}\lora_model_path=${PATH_TO_TRAINED_MODEL}\merged_model_path=${PATH_TO_...
lora_dropout, init_lora_weights, use_rslora, use_dora: bool = False ): # This code wor...
parse_args() merge_lora_weights(args) #!/bin/bash #SBATCH --nodes=1 #SBATCH --exclusive #SBATCH --output=/fsx/ubuntu/peft_ft/logs/8b/lora_weights.log export OMP_NUM_THREADS=1 source $HOME/peft_ft/env_llama3_8B_peft/bin/activate srun python3 "$HOME/peft_ft/merge_lo...
而在LoRa技术(大型语言模型的低秩适应)中不是添加新的层，而是以一种避免在推理阶段出现这种可怕的延迟问题的方式向模型各层参数添加值。LoRa训练并存储附加权重的变化，同时冻结预训练模型的所有权重。也就是说我们利用预训练模型矩阵的变化训练一个新的权重矩阵，并将这个新矩阵分解为2个低秩矩阵，如下所示:LoRA[1...
parser.add_argument('--output_dir',default='output/merge_qwen_14b',type=str)args=parser.parse_args()logger.info(f"merged_args:{args}")base_model_path=args.base_modellora_model_path=args.lora_modeloutput_dir=args.output_dirpeft_config=PeftConfig.from_pretrained(lora_model_path)model_class,...
Feature request Support merging LoRA adapters with base model when base model is loaded in int8. Motivation This is helpful when the goal is to merge adapter weights for faster inference with 8bit model inference. This is helpful for low...
init_lora_weights=False, ) model_new = get_peft_model(model, config, adapter_name="adapter1") model_new.add_adapter("adapter2", config)# adapter1 outputmodel_new.set_adapter("adapter1") output_adapter1 = model_new(input).logitsprint("Model output after loading adapter1:")print(output_...
In this method, you first prune the smallest values of the task weights and retain the top-k values based on the specified fraction density. Then, you carry out the weighted sum of task tensors based on user-specified weightage for participating LoRA adapters. How do I merge ...
model = model.merge_and_unload() logits_merged = model(**dummy_input)[0] self.assertTrue(torch.allclose(logits_unmerged, logits_merged, atol=1e-4, rtol=1e-4)) # For this test to work, init_lora_weights must be False. This ensures that weights are not initialized to # the ...
(Optional) If you need the pre-trained weights from HuggingFace or if you're training a Llama 3.2 model, you must get the HuggingFace token before you start training. For more information about getting the token, seeUser access tokens. ...