···# 使用 get_peft_model 封装 model(顺便添加一个 adapter 名为 "memory_encoder")model = get_peft_model(model, peft_config, adapter_name="memory_encoder")# 然后再添加两个 adapters "get_memory_slot_num" 和 "memory_merge"model.add_adapter(peft_config=peft_config, adapter_name="get_memo...
上述这些流程在add_adapter函数中进行串联起来: defadd_adapter(self,adapter_name,config=None):ifconfigisnotNone:# 基于model config更新lora config,主要补充的是target_module参数model_config=self.model.config.to_dict()ifhasattr(self.model.config,"to_dict")elseself.model.configconfig=self._prepare_lora...
add_adapter(peft_cfg_cls(model_cfg)) 这里是增加adapter,就是Peft的核心的,增加可训练的子模块了。 先是基于model_cfg来构造一个PtuningPEFTConfig配置文件的实例。 然后是,调用add_adapter函数,从而追加可训练的参数到model。 > /workspace/asr/elyza/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_...
config0 = LoraConfig(...) config1 = LoKrConfig(...) peft_model = get_peft_model(base_model, config0, "adapter0", mixed=True) peft_model.add_adapter(config1, "adapter1") peft_model.set_adapter(["adapter0", "adapter1"]) for batch in dataloader: ......
In NeMo 1.0, PEFT is enabled by setting thepeft_schemefield in the YAML file. model:peft:peft_scheme:"lora"restore_from_path:null In code, the only differences between PEFT and full-parameter fine-tuning are theadd_adapterandload_adaptersfunctions. ...
add_argument("--save_format", type=str, choices=["safetensors", "pytorch"]) args = parser.parse_args() output_dir = Path(args.output_dir) checkpoint_dir = Path(args.input_dir) / "adapter_shards" custom_consolidate_to_unified_checkpoint( checkpoint_dir=checkpoint_dir, outpu...
model.add_adapter(adapter_to_delete, config) model.set_adapter(adapter_to_delete) model = model.to(self.torch_device) if config.peft_type not in ("LORA"): with self.assertRaises(AttributeError): model.delete_adapter(adapter_to_delete) ...
Adapter modules were the answer: small add-ons that insert a handful of trainable, task-specific parameters into each transformer layer of the model. LoRA Introduced in 2021, low-rank adaption of large language models (LoRA) uses twin low-rank decomposition matrices to minimize model weights and...
trainer:num_nodes:1devices:8 model:restore_from_path:/path/to/gemma-7b.nemo restore_from_pathsets the path to the.nemocheckpoint to run fine-tuning. peft_schemesets the fine-tuning scheme to be used. Supported schemes include: lora, adapter, ia3, ptuning....
add_adapter PromptEmbedding: class 网络构造思想: peft model: 训练数据 forward peft model 推理greedy_search 总结 导航 迷途小书僮:[代码学习]Huggingface的peft库学习-part 1- prefix tuning 上次是为24层transformer layer,分别追加了30个虚拟token作为额外的可训练的memory。 本次的prompt tuning,则是在prompt...