"vocab_size": 128_256, # Vocabulary size "context_length": 8192, # Context length "emb_dim": 4096, # Embedding dimension "n_heads": 32, # Number of attention heads "n_layers": 32, # Number of layers "hidden_dim": 14_336, # Size of the intermediate dimension in FeedForward "n_...
Vocabulary size 32k,看起来支持中文有限,支持多语言的话,这个值只应该至少在 50k 甚至 100k 以上。 6. 硬件消耗:Meta’s Research Super Cluster和 Internal Production Clusters 的 A100 集群上训练,GPU Hours和模型参数量是线性关系。另外文章计算了碳排放量。 2.2 Pretraining 评估 Code,包括 HumanEval ...
vocab_size (int): Vocabulary size. n_layers (int): Number of layers in the model. tok_embeddings (ParallelEmbedding): Token embeddings. layers (torch.nn.ModuleList): List of Transformer blocks. norm (RMSNorm): Layer normalization for the model output. output (ColumnParallelLinear): ...
在LlaMa 3-8B模型中,这个参数设定为8,000个tokens,即Context Window Size = 8K。这意味着模型在单次处理时可以考虑的最大token数量为8,000。这对于理解长文本或保持长期对话上下文非常关键。 2. Vocabulary-size (词汇量) 这是模型能识别的所有不同token的数量。这包括所有可能的单词、标点符号和特殊字符。模型的...
在Llama2-Chinese目录下创建一个python文件generate.py importtorch fromtransformersimportAutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained('Llama2-Chinese-13b-Chat',device_map='auto',torch_dtype=torch.float16,load_in_8bit=True) ...
LlamaForCausalLM =LlamaModel + lm_head :通过一个线性层将hidden_size映射到vocabulary_size从而得到logits 根据小学二年级学到的Transformer结构,我们可以清晰地看出Llama的模型架构就是经典的Transformer decoder,我们接下来重点介绍llama与transformer decoder之前的区别和改进。
The total vocabulary size is 32k tokens. 2.2.1 Training Hardware & Carbon Footprint Training Hardware. We pretrained our models on Meta’s Research Super Cluster (RSC) (Lee and Sengupta, 2022) as well as internal production clusters. Both clusters use NVIDIA A100s. There are two key ...
vocab_size: vocabulary size init_method: weight initialization method """ def __init__(self, hidden_size, vocab_size, init_method): super(Llama2Embedding, self).__init__() args = get_args() self.hidden_size = hidden_size self.init_method = init_method ...
As we highlighted in our previous blog post, we've integrated extra special tokens to better structure our data. These tokens bump the vocabulary size from 32,000 to 32,004 in the Llama 2 models we're working with. Naturally, this raises the question: Should we train these additional t...
此外,分词器(Tokenizer)是一个软件组件,它能够把你的输入文本转换成一个嵌入,然后由转换器使用它。词汇大小(vocabulary size)是指在其上进行训练的模型的唯一符号的数量。转换器的块结构(block structure)是指为特定模型选择的层、头、激活函数、分词器和层规范化的组合。