我们来算笔账,以Llama 7B模型为例,hidden_size为4096,也就说每个K,V有4096 个数据,假设是半精度浮点数据float16,一个Transformer Block中就有 4096* 2 *2 = 16KB的单序列 K,V缓存空间,而Llama 2一共32个Transformer Block,所以单序列整个模型需要16 * 32 = 512KB的缓存空间,那多序列呢?如果此时句子长度...
以llama7B模型为例,hidden_size为4096,也就是每个K、V有4096个数据,假设半精度浮点数数据float16,一个Transformer Block中就有409622=16KB的单序列KV缓存空间,而llama2一共32个Transformer Block,所以单序列整个模型需要16*32=512KB的缓存空间,那多序列呢?如果此时句子长度为1024,那就得512MB的缓存空间了。而现在...
3.在mindformersr0.8版本llama2中attention计算时,k和v的线形层输入维度为hidden_size,输出维度为n_kv_head * head_dim,已知在llama2中head_dim为128,n_kv_head为32,32 * 128 = 4096 要想适配mistral,手动给n_kv_head赋值为8,这样输出维度就变成了 8 * 128 = 1024,需要注意这里n_kv_head这个参数在...
the hidden size of the pre-trained model output_dim = 768 # e.g., the output size of the...
LLAMA2_CONFIG_7B = {"vocab_size": 32_000,# Vocabulary size"context_length": 4096,# Context length"emb_dim": 4096,# Embedding dimension"n_heads": 32,# Number of attention heads"n_layers": 32,# Number of layers"hidde...
在模型结构上，Skywork-13B 模型采用相对 LLaMA2-13B 更加瘦长的网络结构，层数为 52 层，同时将 FFN Dim 和 Hidden Dim 缩小到 12288 和 4608，从而保证模型参数量和原始 LLaMA-13B 模型相当。根据前期实验，相对瘦长的网络结构在大 Batch Size 训练下可以取得更好的泛化效果。Skywork-13B 和 LLaMA-2-13B ...
for instance, both Llama 1 and Llama 2 projection use 2.7x hidden size rather than the standard 4x hidden size. A key difference between Llama 1 and Llama 2 is the architectural change of attention layer, in which Llama 2 takes advantage of Grouped Query Attention (GQA) mechanism to improve...
value矩阵权重[number_size * dim_hidden * dim_model ] 映射到 维度为[1 * dim_hidden * dim...
"hidden_size": int4096 "initializer_range": float0.02 "intermediate_size": int11008 "max_position_embeddings": int4096 "model_type": string"llama" "num_attention_heads": int32 "num_hidden_layers": int32 "num_key_value_heads": int32 "pretraining_tp": int1 "rms_norm_eps": float0.0000...
{"dim": 4096, "n_layers": 32, "head_dim": 128, "hidden_dim": 14336, "n_heads": 32, "n_kv_heads": 8, "norm_eps": 1e-05, "vocab_size": 32000, "moe": {"num_experts_per_tok": 2, "num_experts": 8} 与GPT-4（网传版）相比，Mistral 8x7B具有类似的架构，但在规模上有...