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PeftModelForCausalLM(
(base_model): LoraModel(
(model): LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(128256, 4096)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(k_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(v_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(o_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(rotary_emb): LlamaExtendedRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(up_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(down_proj): lora.Linear4bit(
(base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Identity()
)
(lora_A): ModuleDict(
(default): Linear(in_features=14336, out_features=16, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=16, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(act_fn): SiLU()
)
(input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)
(post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)
)
)
(norm): LlamaRMSNorm((4096,), eps=1e-05)
(rotary_emb): LlamaRotaryEmbedding()
)
(lm_head): Linear(in_features=4096, out_features=128256, bias=False)
)
)
)
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