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| Layer Name | Role (Short) | Type |
|---|---|---|
q_proj, k_proj, v_proj | Compute query, key, and value for attention mechanism | Attention Proj |
o_proj | Projects attention output back to model hidden size | Attention Proj |
down_proj | Projects MLP output down to hidden size | MLP |
gate_proj | First part of Gated MLP, controls info flow | MLP |
up_proj | Expands hidden size in MLP | MLP |
lm_head | Final linear layer for logits | Output Head |
embed_tokens | Token embedding layer | Input Embed |
norm | Final layernorm | Normalization |
*_layernorm | Normalize inputs to layers | Normalization |
Qwen2ForCausalLM(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 896, padding_idx=151665)
(layers): ModuleList(
(0-23): 24 x Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): Linear(in_features=896, out_features=896, bias=True)
(k_proj): Linear(in_features=896, out_features=128, bias=True)
(v_proj): Linear(in_features=896, out_features=128, bias=True)
(o_proj): Linear(in_features=896, out_features=896, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): Linear(in_features=896, out_features=4864, bias=False)
(up_proj): Linear(in_features=896, out_features=4864, bias=False)
(down_proj): Linear(in_features=4864, out_features=896, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm((896,), eps=1e-06)
(post_attention_layernorm): Qwen2RMSNorm((896,), eps=1e-06)
)
)
(norm): Qwen2RMSNorm((896,), eps=1e-06)
(rotary_emb): LlamaRotaryEmbedding()
)
(lm_head): Linear(in_features=896, out_features=151936, bias=False)
)