prefix_projection:是否投影前缀嵌入(token),默认值为false,表示使用P-Tuning v2, 如果为true,则表示使用 Prefix Tuning。 第三步,通过调用 get_peft_model 方法包装基础的 Transformer 模型。 model = AutoModelForCausalLM.from_pretrained(model_name_or_path) model = get_peft_model(model, peft_config) mode...
self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) self.trans = torch.nn.Sequential( torch.nn.Linear(config.hidden_size, config.prefix_hidden_size...
super().__init__() self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) self.trans = torch.nn.Sequential( torch.nn.Linear(config.hidden_size, c...
self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) self.trans = torch.nn.Sequential( torch.nn.Linear(config.hidden_size, config.prefix_hidden_size...
Kemmar, A., Loudni, S., Lebbah, Y., Boizumault, P., Charnois, T.: Prefix- projection global constraint for sequential pattern mining. In: Principles and Prac- tice of Constraint Programming, Springer (2015)Kemmar, A., Loudni, S., Lebbah, Y., Boizumault, P., Charnois, T....
if self.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens) else: past_key_values = self.embedding(prefix) return past_key_values Adapter Tuning 通过引入少量可训练参数(适配器模块)来进行特定任务的优化。适配器模块是一组轻量级的参数,被添加到模型的...
self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_hidden_layers *2* config.hidden_size)defforward(self, prefix: torch.Tensor):ifself.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens)else: ...
from peft import PrefixTuningConfig, PrefixTuningForCausalLM prefix_tuning_config = PrefixTuningConfig( task_type="CAUSAL_LM", num_virtual_tokens=20, prefix_projection=True ) prefix_tuning_model = PrefixTuningForCausalLM.from_pretrained("modelscope/Llama-2-7b-ms", config=prefix_tuning_config) #...
self.embedding = torch.nn.Embedding(config.prefix_length, config.num_hidden_layers *2* config.hidden_size)defforward(self, prefix: torch.Tensor):ifself.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens)else: ...
很多文章在讲PrefixSpan的时候,不注重基础概念的描述,而只是在乎算法的流程,这样会使得只是照葫芦画瓢,又或者对算法流程某些局部的地方存疑。所以这里重点讲解算法涉及到的所有概念,并且给出经典的Projection过程和理解。 引言: 首先要理解什么是序列模式挖掘问题,然后要清楚算法引出的一些概念前缀、投影、后缀,最后才是整...