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JOSIE(
(encoder): Encoder(
(modality_preprocessors): ModuleDict(
(vision): RGBDTPreprocessor(
(cls_token): tensor((1, 1, 768), requires_grad=False)
(rgbt_stem): PatchEmbedGeneric(
(proj): Sequential(
(0): PadIm2Video()
(1): Conv3d(3, 768, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
)
)
(pos_embedding_helper): SpatioTemporalPosEmbeddingHelper(
(pos_embed): tensor((1, 7681, 768), requires_grad=False)
)
)
(audio): AudioPreprocessor(
(cls_token): tensor((1, 1, 768), requires_grad=False)
(rgbt_stem): PatchEmbedGeneric(
(proj): Conv2d(1, 768, kernel_size=(16, 16), stride=(10, 10), bias=False)
(norm_layer): RMSNorm()
)
(pos_embedding_helper): SpatioTemporalPosEmbeddingHelper(
(pos_embed): tensor((1, 229, 768), requires_grad=False)
)
)
(depth): RGBDTPreprocessor(
(cls_token): tensor((1, 1, 384), requires_grad=False)
(depth_stem): PatchEmbedGeneric(
(proj): Conv2d(1, 384, kernel_size=(16, 16), stride=(16, 16), bias=False)
(norm_layer): RMSNorm()
)
(pos_embedding_helper): SpatioTemporalPosEmbeddingHelper(
(pos_embed): tensor((1, 197, 384), requires_grad=False)
)
)
(thermal): ThermalPreprocessor(
(cls_token): tensor((1, 1, 768), requires_grad=False)
(rgbt_stem): PatchEmbedGeneric(
(proj): Conv2d(1, 768, kernel_size=(16, 16), stride=(16, 16), bias=False)
(norm_layer): RMSNorm()
)
(pos_embedding_helper): SpatioTemporalPosEmbeddingHelper(
(pos_embed): tensor((1, 197, 768), requires_grad=False)
)
)
)
(modality_transformers): ModuleDict(
(vision): EncoderTransformer(
(pre_transformer_layer): Sequential(
(0): RMSNorm()
(1): EinOpsRearrange()
)
(post_transformer_layer): EinOpsRearrange()
(blocks): ModuleList(
(0): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(1): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(2): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(3): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(4): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(5): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(6): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(7): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(8): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(9): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(10): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(11): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
)
)
(audio): EncoderTransformer(
(pre_transformer_layer): Sequential(
(0): RMSNorm()
(1): EinOpsRearrange()
)
(post_transformer_layer): EinOpsRearrange()
(blocks): ModuleList(
(0): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(1): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.009)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(2): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.018)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(3): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.027)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(4): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.036)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(5): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.045)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(6): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.055)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(7): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.064)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(8): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.073)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(9): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.082)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(10): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.091)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(11): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): DropPath(drop_prob=0.100)
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
)
)
(depth): EncoderTransformer(
(pre_transformer_layer): Sequential(
(0): RMSNorm()
(1): EinOpsRearrange()
)
(post_transformer_layer): EinOpsRearrange()
(blocks): ModuleList(
(0): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
(1): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
(2): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
(3): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
(4): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
(5): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=384, out_features=384, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=384, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=384, bias=False)
(w3): Linear(in_features=384, out_features=256, bias=False)
)
)
)
)
(thermal): EncoderTransformer(
(pre_transformer_layer): Sequential(
(0): RMSNorm()
(1): EinOpsRearrange()
)
(post_transformer_layer): EinOpsRearrange()
(blocks): ModuleList(
(0): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(1): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(2): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(3): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(4): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
(5): EncoderTransformerBlock(
(attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(drop_path): Identity()
(norm1): RMSNorm()
(norm2): RMSNorm()
(mlp): MLP(
(w1): Linear(in_features=768, out_features=512, bias=False)
(w2): Linear(in_features=512, out_features=768, bias=False)
(w3): Linear(in_features=768, out_features=512, bias=False)
)
)
)
)
)
(modality_heads): ModuleDict(
(vision): Sequential(
(0): RMSNorm()
(1): SelectElement()
(2): Linear(in_features=768, out_features=1024, bias=False)
)
(audio): Sequential(
(0): RMSNorm()
(1): SelectElement()
(2): Linear(in_features=768, out_features=1024, bias=False)
)
(depth): Sequential(
(0): RMSNorm()
(1): SelectElement()
(2): Linear(in_features=384, out_features=1024, bias=False)
)
(thermal): Sequential(
(0): RMSNorm()
(1): SelectElement()
(2): Linear(in_features=768, out_features=1024, bias=False)
)
)
)
(reasoner): Qwen2ForCausalLM(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 896)
(layers): ModuleList(
(0): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(1): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(2): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(3): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(4): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(5): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(6): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(7): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(8): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(9): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(10): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(11): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(12): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(13): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(14): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(15): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(16): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(17): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(18): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(19): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(20): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(21): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(22): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
(23): 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): Qwen2RotaryEmbedding()
)
(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()
(post_attention_layernorm): Qwen2RMSNorm()
)
)
(norm): Qwen2RMSNorm()
)
(lm_head): Linear(in_features=896, out_features=151936, bias=False)
)
(input_projetor): Linear(in_features=1024, out_features=896, bias=True)
) |