def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value 相关的方法分别是 get_input_embeddings 和set_input_embeddings,分别用于获取和设置 self.embed_tokens 3、 注意力掩码: # Copied from transformers.models.bart.modeling_bart.Bart...
get_input_embeddings() # 新完全初始化的embedding new_vocab_size = len(new_tokenizer) embedding_dim = 4096 new_embedding = torch.nn.Embedding(new_vocab_size, embedding_dim) # 将现有Embedding层的权重赋值给新的Embedding层的前32000行 num_to_copy = min(new_vocab_size, len(embeddings.weight))...
embedding_size = base_model.get_input_embeddings().weight.size(1) model_size = emb_to_model_size[embedding_size] print(f"Peft version: {peft.__version__}") print(f"Loading LoRA for {model_size} model") lora_model = None lora_model_sd = None for lora_index, lora_model_path in ...
self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self....
model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): ...
documents = SimpleDirectoryReader(input_files=['./data/file.txt']).load_data() 1. 2. 3. 4. 5. 6. 7. 或者直接把自己的text改为document文档 from llama_index import Document # 直接从文本转换 text_list = [text1, text2, ...]
Multiline input For multiline input, you can wrap text with""": >>> """Hello, ... world! ... """ I'm a basic program that prints the famous "Hello, world!" message to the console. Multimodal models ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"...
Finally, we referencedPALMand employed Shared Input-Output Embeddings. Pre-training We use multi-GPU parallel training based on the Accelerate library, with the following start command: accelerate launch --config_file configs/accelerate_configs/ds_stage1.yaml train_lm.py --train_config configs/pre...
在位置编码上,使用旋转位置嵌入(Rotary Positional Embeddings,RoPE)[52] 代替原有的绝对位置编码。RoPE 借助了复数的思想,出发点是通过绝对位置编码的方式实现相对位置编码。其目标是通过下述运算来给q,k 添加绝对位置信息: 经过上述操作后, ˜qm 和˜kn 就带有位置m 和n 的绝对位置信息。
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) ...