···# 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...
Model: "mistralai/Mistral-7B-v0.1" Quantization: BitsAndBytes Peft_ adapter: Lora Additionally, I have followed the example provided on the HuggingFace platform, available at their documentation site (https://huggingface.co/docs/transformers/main/peft) but, I encountered the same issue. ...
add_adapter(peft_config) # 接下来的训练代码 \quad 为了推理使用新训练的模型,AutoModel类在后端使用PEFT来加载适配器权重和配置文件。 from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora") # 接下来的推理代码 如果您想要比较或使用多个适配...
get_peft_model 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 tuni...
│ ❱ 166 │ │ model.load_adapter(model_id, adapter_name, **kwargs) │ │ 167 │ │ return model │ │ 168 │ │ │ 169 │ def _setup_prompt_encoder(self, adapter_name): │ │ │ │ /usr/local/lib/python3.7/site-packages/peft/peft_model.py:355 in load_adapter │ ...
def _test_adapter_name(self, model_id, config_cls, config_kwargs): model = self.transformers_class.from_pretrained(model_id) config = config_cls( base_model_name_or_path=model_id, **config_kwargs, ) model = get_peft_model(model, config, adapter_name="test-adapter") ...
在peft中使用LoRA非常简单。借助PeftModel抽象,可以快速将低秩适配器(LoRA)应用到任意模型中。 在初始化相应的微调配置类(LoraConfig)时,需要显式指定在哪些层新增适配器(Adapter),并将其设置正确。 ChatGLM3-6B模型通过以下方式获取需要训练的模型层的名字 ...
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. # In NeMo 2.0, PEFT is enabled by passing in the PEFT method callback to both the trainer and model: ...
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. NeMo 2.0 (New Release) In NeMo 2.0, PEFT is enabled by passing in the PEFT method callback to both the trainer and...
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...