qwen2¶
- torchtune.models.qwen2.qwen2(vocab_size: int, num_layers: int, num_heads: int, num_kv_heads: int, embed_dim: int, intermediate_dim: int, max_seq_len: int, attn_dropout: float = 0.0, norm_eps: float = 1e-05, rope_base: float = 1000000.0, tie_word_embeddings: bool = False) TransformerDecoder[source]¶
Build the decoder associated with the Qwen2 model. This includes: - Token embeddings - num_layers number of TransformerSelfAttentionLayer blocks - RMS Norm layer applied to the output of the transformer - Final projection into token space
- Parameters:
vocab_size (int) – number of tokens in vocabulary.
num_layers (int) – number of layers in the transformer decoder.
num_heads (int) – number of query heads. For MHA this is also the number of heads for key and value
num_kv_heads (int) – number of key and value heads. User should ensure num_heads % num_kv_heads == 0. For standard MHA set num_kv_heads == num_heads, for GQA num_kv_heads < num_heads, and for MQA set num_kv_heads == 1.
embed_dim (int) – embedding dimension for self-attention
max_seq_len (int) – maximum sequence length the model will be run with, as used by
KVCache()attn_dropout (float) – dropout value passed onto scaled_dot_product_attention. Default: 0.0
intermediate_dim (Optional[int]) – intermediate dimension for MLP. If not specified, this is computed using
scale_hidden_dim_for_mlp()norm_eps (float) – epsilon in RMS norms.
rope_base (float) – the base period of the RoPE embeddings.
tie_word_embeddings (bool) – whether the model’s input and output word embeddings should be tied.
- Returns:
Instantiation of Qwen2 model.
- Return type:
