( model_name, load_in_8bit=True, device_map="auto", use_auth_token=True ) model = PeftModel.from_pretrained(model, "tloen/alpaca-lora-7b", adapter_name="eng_alpaca") model.load_adapter("22h/cabrita-lora-v0-1", adapter_name="portuguese_alpaca") model.set_adapter("eng_alpaca") ...
LoRA对参数矩阵进行低秩分解; 在训练时,固定住原参数矩阵,仅训练分解出的两个矩阵。是一种大规模减少 LLM 训练参数的方法。LoRA 已被集成在了PEFT库里,使用非常方便: from transformers import AutoModelForVision2Seq + from peft import get_peft_model, LoraConfig model = AutoModelForVision2Seq.from_pretrained...
而huggingface的PEFT库特别适合各种微调任务,包括lora在内的很多微调方法 LoRA(Low-Rank Adaptation)是一种技术,通过低秩分解将权重更新表示为两个较小的矩阵(称为更新矩阵),从而加速大型模型的微调,并减少内存消耗 为了使微调更加高效,LoRA的方法是通过低秩分解,使用两个较小的矩阵(称为更新矩阵)来表示权重更新 这些...
pip install peft Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration withget_peft_model. For the bigscience/mt0-large model, you're only training 0.19% of the parameters!
src/peft tuners adalora adaption_prompt ia3 loha lokr lora __init__.py bnb.py config.py gptq.py layer.py model.py multitask_prompt_tuning p_tuning prefix_tuning prompt_tuning __init__.py lycoris_utils.py tuners_utils.py utils
I have fine-tuned the model using Lora, the config is available here: "Lukee4/biogpt-2020_2labels" I used BioGPTforSequenceClassification and the fine-tuning worked fine, the results on the test data improved after fine-tuning in compari...
[LoRA](https://arxiv.org/abs/2106.09685) 对参数矩阵进行低秩分解; 在训练时,固定住原参数矩阵,仅训练分解出的两个矩阵。是一种大规模减少 LLM 训练参数的方法。LoRA 已被集成在了 [PEFT](https://github.com/huggingface/peft) 库里,使用非常方便: ```diff from transformers import AutoModelForVision2Seq...
This is a basis for discussion and not yet ready to be merged. There are surprisingly few changes necessary to make LoRA work with custom models. Those changes mostly revolve around attributes or m...
0.3, orth_reg_weight=0.2, # lora_alpha=32, # lora_dropout=0.05, bias="none", task_type=TaskType.CAUSAL_LM, target_modules=["query_key_value"], inference_mode=False, r=lora_r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, ) lora_model = get_peft_model(glm_model, lora_config...
tuners.lora.layer import LoraLayer from peft.tuners.tuners_utils import BaseTunerLayer if is_aqlm_available(): from aqlm import QuantizedLinear class AqlmLoraLinear(torch.nn.Module, LoraLayer): def __init__( self, base_layer, adapter_name: str, r: int = 0, lora_alpha: int = 1, ...