model.resize_token_embeddings() 是不是只会对新增的token初始化权重? 方式2,3 使用model.resize_token_embeddings()后会改变原始模型的embeddings size,这会导致后续微调模型的效果变得很差吗? 回答: 从让transformers模型新增token角度,这两都可以实现,没有区别。 是的,结合上面关于model.resize_token_embeddings...
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: """ Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`. Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method...
tokenizer.add_tokens(["newword","awdddd"])print(len(tokenizer)) x = model.embeddings.word_embeddings.weight[-1, :]# 扩展模型的嵌入矩阵,以包含新词汇的嵌入向量(重要)model.resize_token_embeddings(len(tokenizer)) y = model.embeddings.word_embeddings.weight[-2, :] z = model.embeddings.word_...
cache_dir="/data0/xp/gec/model") model.resize_token_embeddings(len(tokenizer)) configuration = model.config # Data collator data_collator = DataCollatorForSeq2Seq( tokenizer, model=model, ) # Definite training arguments training_args = Seq2SeqTrainingArguments( output_dir=OUTPUT_DIR, learning_...
I have added the flag to resize_token_embeddings, _get_resized_embeddings, and _get_resized_lm_head. _get_resized_lm_head is very important for models that have untied weights. This should have been added from the beginning but I don't know how I didn't notice that. 👍 1 Contribut...
model.resize_token_embeddings(len(tokenizer)) # print("num_labels=", model.num_labels) tokenized_datasets = processor.get_tokenized_datasets() if training_args.do_train: if "train" not in tokenized_datasets: raise ValueError("--do_train requires a train dataset") ...
3. 使用tokenizer方法：调用tokenizer特定方法，加入新[token]，同样需要调整模型权重。大部分现代LLM模型已无法通过直接修改词汇表添加新自定义[token]，方法1已不可行。方法2和3效果相当，均需调用model.resize_token_embeddings()。执行model.resize_token_embeddings()时，若新增[token]数量超过最大单词数...
:obj:`torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model. """ base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed model_embeds = base_model._resize_token_embeddings(new_num_tokens) ...
KubeGems 在1.23版本中加入了模型商店的功能，其主要目的就是为了让开发者快速部署和体验这些优秀的模型...
请注意,多张图像会导致输入 token 数量增加,因此通常需要增加上下文窗口的大小。from lmdeploy import pipeline, TurbomindEngineConfig from lmdeploy.vl import load_image from lmdeploy.vl.constants import IMAGE_TOKEN model = 'OpenGVLab/InternVL2-8B' pipe = pipeline(model, backend_config=TurbomindEngine...