也许这就是你正在寻找的add_weighted_adapter。你可以参考这个文档和这个issue
也许这就是你正在寻找的add_weighted_adapter。你可以参考这个文档和这个issue
Use [`~LoraModel.delete_adapter`] to delete an existing adapter. Use [`~LoraModel.add_weighted_adapter`] to combine multiple LoRAs into a new adapter based on the user provided weighing scheme. ## Common LoRA parameters in PEFT As with other methods supported by PEFT, to fine-tune a mod...
The idea is that by learning the weights used for the weighted average (the weights argument for add_weighted_adapter) can lead to better results than naive uniform weights. (Note that we offer many combination types, not just averaging, maybe that's worth looking into for the paper.) To ...
there is no direct ways to bypass ´ the extra compute in adapter layers. This seems like a non-issue since adapter layers are designed to have few parameters (sometimes <1% of the original model) by having a small bottleneck dimension, which limits the FLOPs they can add. However, large...
center)*2# 计算 adapter 权重weights=torch.softmax(torch.stack([a['similarity']forainadapters]),dim=0)expert_alphas=weights.mul(16//len(adapters)).tolist()unique_name=str(hash(datetime.datetime.now()))# 这里相对于设置一个 当前策略的apater ,名字不是固定的main_model.add_weighted_adapter([...
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class WeightedCELossTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): labels = inputs.pop("labels") # Get model's predictions outputs = model(**inputs) logits = outputs.get("logits") # Compute custom loss loss_fct = torch.nn.CrossEntropyLoss(weight=torch....
res = test_fiqa(model, tokenizer, prompt_fun = add_instructions, batch_size = batch_size) # NWGI, len 4047 res = test_nwgi(model, tokenizer, batch_size = batch_size) 5.3 与FinGPT V3.1结果进行比较 解释说明: TFNS:本笔记本的测试结果相对较好,因为使用了TFNS数据集进行训练。