<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (uint8[batch,224,224,3] image) => (float[batch,512] image_embedding) 
   <
      float[batch,50,768] add_1088
      float[batch,50,768] add_1109
      float[batch,50,768] add_1224
      float[batch,50,768] add_1245
      float[batch,50,768] add_136
      float[batch,50,768] add_1360
      float[batch,50,768] add_1381
      float[batch,50,768] add_1496
      float[batch,50,768] add_1517
      float[batch,1,768] add_1517_pooled
      float[batch,50,768] add_157
      float[batch,1,768] add_1632
      float[batch,1,768] add_1653
      float[batch,50,768] add_17
      float[batch,50,768] add_272
      float[batch,50,768] add_293
      float[batch,50,768] add_408
      float[batch,50,768] add_429
      float[batch,50,768] add_544
      float[batch,50,768] add_565
      float[batch,50,768] add_680
      float[batch,50,768] add_701
      float[batch,50,768] add_816
      float[batch,50,768] add_837
      float[batch,50,768] add_952
      float[batch,50,768] add_973
      float[batch,1] clamp_min
      float[batch,768,7,7] conv2d
      float[batch,50,3072] gelu
      float[batch,50,3072] gelu_1
      float[batch,50,3072] gelu_10
      float[batch,1,3072] gelu_11
      float[batch,50,3072] gelu_2
      float[batch,50,3072] gelu_3
      float[batch,50,3072] gelu_4
      float[batch,50,3072] gelu_5
      float[batch,50,3072] gelu_6
      float[batch,50,3072] gelu_7
      float[batch,50,3072] gelu_8
      float[batch,50,3072] gelu_9
      float[batch,3,224,224] image_chw
      float[batch,224,224,3] image_f32
      float[batch,50,768] layer_norm
      float[batch,50,768] layer_norm_1
      float[batch,50,768] layer_norm_10
      float[batch,50,768] layer_norm_11
      float[batch,50,768] layer_norm_12
      float[batch,50,768] layer_norm_13
      float[batch,50,768] layer_norm_14
      float[batch,50,768] layer_norm_15
      float[batch,50,768] layer_norm_16
      float[batch,50,768] layer_norm_17
      float[batch,50,768] layer_norm_18
      float[batch,50,768] layer_norm_19
      float[batch,50,768] layer_norm_2
      float[batch,50,768] layer_norm_20
      float[batch,50,768] layer_norm_21
      float[batch,50,768] layer_norm_22
      float[batch,50,768] layer_norm_23
      float[batch,1,768] layer_norm_24
      float[batch,50,768] layer_norm_3
      float[batch,50,768] layer_norm_4
      float[batch,50,768] layer_norm_5
      float[batch,50,768] layer_norm_6
      float[batch,50,768] layer_norm_7
      float[batch,50,768] layer_norm_8
      float[batch,50,768] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,50,3072] linear_10
      float[batch,50,768] linear_11
      float[batch,50,3072] linear_14
      float[batch,50,768] linear_15
      float[batch,50,3072] linear_18
      float[batch,50,768] linear_19
      float[batch,50,3072] linear_2
      float[batch,50,3072] linear_22
      float[batch,50,768] linear_23
      float[batch,50,3072] linear_26
      float[batch,50,768] linear_27
      float[batch,50,768] linear_3
      float[batch,50,3072] linear_30
      float[batch,50,768] linear_31
      float[batch,50,3072] linear_34
      float[batch,50,768] linear_35
      float[batch,50,3072] linear_38
      float[batch,50,768] linear_39
      float[batch,50,3072] linear_42
      float[batch,50,768] linear_43
      float[batch,1,3072] linear_46
      float[batch,1,768] linear_47
      float[batch,50,3072] linear_6
      float[batch,50,768] linear_7
      float[batch,512] matmul
      float[batch,50,768] node_scaled_dot_product_attention_10_k
      float[batch,50,768] node_scaled_dot_product_attention_10_out
      float[batch,50,768] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_10_q
      float[batch,50,2304] node_scaled_dot_product_attention_10_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_10_v
      float[batch,50,768] node_scaled_dot_product_attention_11_k
      float[batch,1,768] node_scaled_dot_product_attention_11_out
      float[batch,1,768] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_11_q
      float[batch,1,768] node_scaled_dot_product_attention_11_q_pooled
      float[batch,50,2304] node_scaled_dot_product_attention_11_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_11_v
      float[batch,50,768] node_scaled_dot_product_attention_1_k
      float[batch,50,768] node_scaled_dot_product_attention_1_out
      float[batch,50,768] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_1_q
      float[batch,50,2304] node_scaled_dot_product_attention_1_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_1_v
      float[batch,50,768] node_scaled_dot_product_attention_2_k
      float[batch,50,768] node_scaled_dot_product_attention_2_out
      float[batch,50,768] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_2_q
      float[batch,50,2304] node_scaled_dot_product_attention_2_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_2_v
      float[batch,50,768] node_scaled_dot_product_attention_3_k
      float[batch,50,768] node_scaled_dot_product_attention_3_out
      float[batch,50,768] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_3_q
      float[batch,50,2304] node_scaled_dot_product_attention_3_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_3_v
      float[batch,50,768] node_scaled_dot_product_attention_4_k
      float[batch,50,768] node_scaled_dot_product_attention_4_out
      float[batch,50,768] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_4_q
      float[batch,50,2304] node_scaled_dot_product_attention_4_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_4_v
      float[batch,50,768] node_scaled_dot_product_attention_5_k
      float[batch,50,768] node_scaled_dot_product_attention_5_out
      float[batch,50,768] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_5_q
      float[batch,50,2304] node_scaled_dot_product_attention_5_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_5_v
      float[batch,50,768] node_scaled_dot_product_attention_6_k
      float[batch,50,768] node_scaled_dot_product_attention_6_out
      float[batch,50,768] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_6_q
      float[batch,50,2304] node_scaled_dot_product_attention_6_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_6_v
      float[batch,50,768] node_scaled_dot_product_attention_7_k
      float[batch,50,768] node_scaled_dot_product_attention_7_out
      float[batch,50,768] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_7_q
      float[batch,50,2304] node_scaled_dot_product_attention_7_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_7_v
      float[batch,50,768] node_scaled_dot_product_attention_8_k
      float[batch,50,768] node_scaled_dot_product_attention_8_out
      float[batch,50,768] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_8_q
      float[batch,50,2304] node_scaled_dot_product_attention_8_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_8_v
      float[batch,50,768] node_scaled_dot_product_attention_9_k
      float[batch,50,768] node_scaled_dot_product_attention_9_out
      float[batch,50,768] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_9_q
      float[batch,50,2304] node_scaled_dot_product_attention_9_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_9_v
      float[batch,50,768] node_scaled_dot_product_attention_k
      float[batch,50,768] node_scaled_dot_product_attention_out
      float[batch,50,768] node_scaled_dot_product_attention_out_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_q
      float[batch,50,2304] node_scaled_dot_product_attention_qkv
      float[batch,50,2304] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,50,768] node_scaled_dot_product_attention_v
      float[batch,49,768] permute
      float[batch,50,768] scaled_dot_product_attention
      float[batch,50,768] scaled_dot_product_attention_1
      float[batch,50,768] scaled_dot_product_attention_10
      float[batch,1,768] scaled_dot_product_attention_11
      float[batch,50,768] scaled_dot_product_attention_2
      float[batch,50,768] scaled_dot_product_attention_3
      float[batch,50,768] scaled_dot_product_attention_4
      float[batch,50,768] scaled_dot_product_attention_5
      float[batch,50,768] scaled_dot_product_attention_6
      float[batch,50,768] scaled_dot_product_attention_7
      float[batch,50,768] scaled_dot_product_attention_8
      float[batch,50,768] scaled_dot_product_attention_9
      float[batch,768] select_36
      float[batch,50,768] val_43
      float[batch,50,3072] val_44
      float[batch,50,768] val_45
      float[batch,50,3072] val_46
      float[batch,50,768] val_47
      float[batch,50,3072] val_48
      float[batch,50,768] val_49
      float[batch,50,3072] val_50
      float[batch,50,768] val_51
      float[batch,50,3072] val_52
      float[batch,50,768] val_53
      float[batch,50,3072] val_54
      float[batch,50,768] val_55
      float[batch,50,3072] val_56
      float[batch,50,768] val_57
      float[batch,50,3072] val_58
      float[batch,50,768] val_59
      float[batch,50,3072] val_60
      float[batch,50,768] val_61
      float[batch,50,3072] val_62
      float[batch,50,768] val_63
      float[batch,50,3072] val_64
      float[batch,50,768] val_65
      float[batch,1,3072] val_66
      float[batch,1,768] val_67
      float[batch,768] val_68
      float[batch,768,49] view
   >
{
   [pre_cast] image_f32 = Cast <to: int = 1> (image)
   [pre_nhwc_to_nchw] image_chw = Transpose <perm: ints = [0, 3, 1, 2]> (image_f32)
   conv2d = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [32, 32]> (image_chw, "visual.conv1.weight", node_Conv_704_fused_bias)
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_permute] permute = Transpose <perm: ints = [0, 2, 1]> (view)
   val_43 = Pad (permute, val_3, val_4)
   add_17 = Add (val_43, val_42)
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_17, "visual.ln_pre.weight", "visual.ln_pre.bias")
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (layer_norm, "visual.transformer.resblocks.0.ln_1.weight", "visual.transformer.resblocks.0.ln_1.bias")
   [node_scaled_dot_product_attention_qkv_mm] node_scaled_dot_product_attention_qkv_mm_out = MatMul (layer_norm_1, val_6)
   [node_scaled_dot_product_attention_qkv_bias] node_scaled_dot_product_attention_qkv = Add (node_scaled_dot_product_attention_qkv_mm_out, "visual.transformer.resblocks.0.attn.in_proj_bias")
   [node_scaled_dot_product_attention_qkv_split] node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v = Split <axis: int = -1> (node_scaled_dot_product_attention_qkv, attn3d_split_3x768)
   [node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_q, node_scaled_dot_product_attention_k, node_scaled_dot_product_attention_v)
   [node_scaled_dot_product_attention_out_mm] node_scaled_dot_product_attention_out_mm_out = MatMul (scaled_dot_product_attention, node_scaled_dot_product_attention_wo_t)
   [node_scaled_dot_product_attention_out_bias] node_scaled_dot_product_attention_out = Add (node_scaled_dot_product_attention_out_mm_out, "visual.transformer.resblocks.0.attn.out_proj.bias")
   add_136 = Add (layer_norm, node_scaled_dot_product_attention_out)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_136, "visual.transformer.resblocks.0.ln_2.weight", "visual.transformer.resblocks.0.ln_2.bias")
   val_44 = MatMul (layer_norm_2, val_7)
   linear_2 = Add (val_44, "visual.transformer.resblocks.0.mlp.c_fc.bias")
   [node_gelu] gelu = Gelu <approximate: string = "none"> (linear_2)
   val_45 = MatMul (gelu, val_8)
   linear_3 = Add (val_45, "visual.transformer.resblocks.0.mlp.c_proj.bias")
   add_157 = Add (add_136, linear_3)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_157, "visual.transformer.resblocks.1.ln_1.weight", "visual.transformer.resblocks.1.ln_1.bias")
   [node_scaled_dot_product_attention_1_qkv_mm] node_scaled_dot_product_attention_1_qkv_mm_out = MatMul (layer_norm_3, val_9)
   [node_scaled_dot_product_attention_1_qkv_bias] node_scaled_dot_product_attention_1_qkv = Add (node_scaled_dot_product_attention_1_qkv_mm_out, "visual.transformer.resblocks.1.attn.in_proj_bias")
   [node_scaled_dot_product_attention_1_qkv_split] node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v = Split <axis: int = -1> (node_scaled_dot_product_attention_1_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_1_q, node_scaled_dot_product_attention_1_k, node_scaled_dot_product_attention_1_v)
   [node_scaled_dot_product_attention_1_out_mm] node_scaled_dot_product_attention_1_out_mm_out = MatMul (scaled_dot_product_attention_1, node_scaled_dot_product_attention_1_wo_t)
   [node_scaled_dot_product_attention_1_out_bias] node_scaled_dot_product_attention_1_out = Add (node_scaled_dot_product_attention_1_out_mm_out, "visual.transformer.resblocks.1.attn.out_proj.bias")
   add_272 = Add (add_157, node_scaled_dot_product_attention_1_out)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_272, "visual.transformer.resblocks.1.ln_2.weight", "visual.transformer.resblocks.1.ln_2.bias")
   val_46 = MatMul (layer_norm_4, val_10)
   linear_6 = Add (val_46, "visual.transformer.resblocks.1.mlp.c_fc.bias")
   gelu_1 = Gelu <approximate: string = "none"> (linear_6)
   val_47 = MatMul (gelu_1, val_11)
   linear_7 = Add (val_47, "visual.transformer.resblocks.1.mlp.c_proj.bias")
   add_293 = Add (add_272, linear_7)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_293, "visual.transformer.resblocks.2.ln_1.weight", "visual.transformer.resblocks.2.ln_1.bias")
   [node_scaled_dot_product_attention_2_qkv_mm] node_scaled_dot_product_attention_2_qkv_mm_out = MatMul (layer_norm_5, val_12)
   [node_scaled_dot_product_attention_2_qkv_bias] node_scaled_dot_product_attention_2_qkv = Add (node_scaled_dot_product_attention_2_qkv_mm_out, "visual.transformer.resblocks.2.attn.in_proj_bias")
   [node_scaled_dot_product_attention_2_qkv_split] node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v = Split <axis: int = -1> (node_scaled_dot_product_attention_2_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_2_q, node_scaled_dot_product_attention_2_k, node_scaled_dot_product_attention_2_v)
   [node_scaled_dot_product_attention_2_out_mm] node_scaled_dot_product_attention_2_out_mm_out = MatMul (scaled_dot_product_attention_2, node_scaled_dot_product_attention_2_wo_t)
   [node_scaled_dot_product_attention_2_out_bias] node_scaled_dot_product_attention_2_out = Add (node_scaled_dot_product_attention_2_out_mm_out, "visual.transformer.resblocks.2.attn.out_proj.bias")
   add_408 = Add (add_293, node_scaled_dot_product_attention_2_out)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_408, "visual.transformer.resblocks.2.ln_2.weight", "visual.transformer.resblocks.2.ln_2.bias")
   val_48 = MatMul (layer_norm_6, val_13)
   linear_10 = Add (val_48, "visual.transformer.resblocks.2.mlp.c_fc.bias")
   gelu_2 = Gelu <approximate: string = "none"> (linear_10)
   val_49 = MatMul (gelu_2, val_14)
   linear_11 = Add (val_49, "visual.transformer.resblocks.2.mlp.c_proj.bias")
   add_429 = Add (add_408, linear_11)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_429, "visual.transformer.resblocks.3.ln_1.weight", "visual.transformer.resblocks.3.ln_1.bias")
   [node_scaled_dot_product_attention_3_qkv_mm] node_scaled_dot_product_attention_3_qkv_mm_out = MatMul (layer_norm_7, val_15)
   [node_scaled_dot_product_attention_3_qkv_bias] node_scaled_dot_product_attention_3_qkv = Add (node_scaled_dot_product_attention_3_qkv_mm_out, "visual.transformer.resblocks.3.attn.in_proj_bias")
   [node_scaled_dot_product_attention_3_qkv_split] node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v = Split <axis: int = -1> (node_scaled_dot_product_attention_3_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_3_q, node_scaled_dot_product_attention_3_k, node_scaled_dot_product_attention_3_v)
   [node_scaled_dot_product_attention_3_out_mm] node_scaled_dot_product_attention_3_out_mm_out = MatMul (scaled_dot_product_attention_3, node_scaled_dot_product_attention_3_wo_t)
   [node_scaled_dot_product_attention_3_out_bias] node_scaled_dot_product_attention_3_out = Add (node_scaled_dot_product_attention_3_out_mm_out, "visual.transformer.resblocks.3.attn.out_proj.bias")
   add_544 = Add (add_429, node_scaled_dot_product_attention_3_out)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_544, "visual.transformer.resblocks.3.ln_2.weight", "visual.transformer.resblocks.3.ln_2.bias")
   val_50 = MatMul (layer_norm_8, val_16)
   linear_14 = Add (val_50, "visual.transformer.resblocks.3.mlp.c_fc.bias")
   gelu_3 = Gelu <approximate: string = "none"> (linear_14)
   val_51 = MatMul (gelu_3, val_17)
   linear_15 = Add (val_51, "visual.transformer.resblocks.3.mlp.c_proj.bias")
   add_565 = Add (add_544, linear_15)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_565, "visual.transformer.resblocks.4.ln_1.weight", "visual.transformer.resblocks.4.ln_1.bias")
   [node_scaled_dot_product_attention_4_qkv_mm] node_scaled_dot_product_attention_4_qkv_mm_out = MatMul (layer_norm_9, val_18)
   [node_scaled_dot_product_attention_4_qkv_bias] node_scaled_dot_product_attention_4_qkv = Add (node_scaled_dot_product_attention_4_qkv_mm_out, "visual.transformer.resblocks.4.attn.in_proj_bias")
   [node_scaled_dot_product_attention_4_qkv_split] node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v = Split <axis: int = -1> (node_scaled_dot_product_attention_4_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_4_q, node_scaled_dot_product_attention_4_k, node_scaled_dot_product_attention_4_v)
   [node_scaled_dot_product_attention_4_out_mm] node_scaled_dot_product_attention_4_out_mm_out = MatMul (scaled_dot_product_attention_4, node_scaled_dot_product_attention_4_wo_t)
   [node_scaled_dot_product_attention_4_out_bias] node_scaled_dot_product_attention_4_out = Add (node_scaled_dot_product_attention_4_out_mm_out, "visual.transformer.resblocks.4.attn.out_proj.bias")
   add_680 = Add (add_565, node_scaled_dot_product_attention_4_out)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_680, "visual.transformer.resblocks.4.ln_2.weight", "visual.transformer.resblocks.4.ln_2.bias")
   val_52 = MatMul (layer_norm_10, val_19)
   linear_18 = Add (val_52, "visual.transformer.resblocks.4.mlp.c_fc.bias")
   gelu_4 = Gelu <approximate: string = "none"> (linear_18)
   val_53 = MatMul (gelu_4, val_20)
   linear_19 = Add (val_53, "visual.transformer.resblocks.4.mlp.c_proj.bias")
   add_701 = Add (add_680, linear_19)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_701, "visual.transformer.resblocks.5.ln_1.weight", "visual.transformer.resblocks.5.ln_1.bias")
   [node_scaled_dot_product_attention_5_qkv_mm] node_scaled_dot_product_attention_5_qkv_mm_out = MatMul (layer_norm_11, val_21)
   [node_scaled_dot_product_attention_5_qkv_bias] node_scaled_dot_product_attention_5_qkv = Add (node_scaled_dot_product_attention_5_qkv_mm_out, "visual.transformer.resblocks.5.attn.in_proj_bias")
   [node_scaled_dot_product_attention_5_qkv_split] node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v = Split <axis: int = -1> (node_scaled_dot_product_attention_5_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_5_q, node_scaled_dot_product_attention_5_k, node_scaled_dot_product_attention_5_v)
   [node_scaled_dot_product_attention_5_out_mm] node_scaled_dot_product_attention_5_out_mm_out = MatMul (scaled_dot_product_attention_5, node_scaled_dot_product_attention_5_wo_t)
   [node_scaled_dot_product_attention_5_out_bias] node_scaled_dot_product_attention_5_out = Add (node_scaled_dot_product_attention_5_out_mm_out, "visual.transformer.resblocks.5.attn.out_proj.bias")
   add_816 = Add (add_701, node_scaled_dot_product_attention_5_out)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_816, "visual.transformer.resblocks.5.ln_2.weight", "visual.transformer.resblocks.5.ln_2.bias")
   val_54 = MatMul (layer_norm_12, val_22)
   linear_22 = Add (val_54, "visual.transformer.resblocks.5.mlp.c_fc.bias")
   gelu_5 = Gelu <approximate: string = "none"> (linear_22)
   val_55 = MatMul (gelu_5, val_23)
   linear_23 = Add (val_55, "visual.transformer.resblocks.5.mlp.c_proj.bias")
   add_837 = Add (add_816, linear_23)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_837, "visual.transformer.resblocks.6.ln_1.weight", "visual.transformer.resblocks.6.ln_1.bias")
   [node_scaled_dot_product_attention_6_qkv_mm] node_scaled_dot_product_attention_6_qkv_mm_out = MatMul (layer_norm_13, val_24)
   [node_scaled_dot_product_attention_6_qkv_bias] node_scaled_dot_product_attention_6_qkv = Add (node_scaled_dot_product_attention_6_qkv_mm_out, "visual.transformer.resblocks.6.attn.in_proj_bias")
   [node_scaled_dot_product_attention_6_qkv_split] node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v = Split <axis: int = -1> (node_scaled_dot_product_attention_6_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_6_q, node_scaled_dot_product_attention_6_k, node_scaled_dot_product_attention_6_v)
   [node_scaled_dot_product_attention_6_out_mm] node_scaled_dot_product_attention_6_out_mm_out = MatMul (scaled_dot_product_attention_6, node_scaled_dot_product_attention_6_wo_t)
   [node_scaled_dot_product_attention_6_out_bias] node_scaled_dot_product_attention_6_out = Add (node_scaled_dot_product_attention_6_out_mm_out, "visual.transformer.resblocks.6.attn.out_proj.bias")
   add_952 = Add (add_837, node_scaled_dot_product_attention_6_out)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_952, "visual.transformer.resblocks.6.ln_2.weight", "visual.transformer.resblocks.6.ln_2.bias")
   val_56 = MatMul (layer_norm_14, val_25)
   linear_26 = Add (val_56, "visual.transformer.resblocks.6.mlp.c_fc.bias")
   gelu_6 = Gelu <approximate: string = "none"> (linear_26)
   val_57 = MatMul (gelu_6, val_26)
   linear_27 = Add (val_57, "visual.transformer.resblocks.6.mlp.c_proj.bias")
   add_973 = Add (add_952, linear_27)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_973, "visual.transformer.resblocks.7.ln_1.weight", "visual.transformer.resblocks.7.ln_1.bias")
   [node_scaled_dot_product_attention_7_qkv_mm] node_scaled_dot_product_attention_7_qkv_mm_out = MatMul (layer_norm_15, val_27)
   [node_scaled_dot_product_attention_7_qkv_bias] node_scaled_dot_product_attention_7_qkv = Add (node_scaled_dot_product_attention_7_qkv_mm_out, "visual.transformer.resblocks.7.attn.in_proj_bias")
   [node_scaled_dot_product_attention_7_qkv_split] node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v = Split <axis: int = -1> (node_scaled_dot_product_attention_7_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_7_q, node_scaled_dot_product_attention_7_k, node_scaled_dot_product_attention_7_v)
   [node_scaled_dot_product_attention_7_out_mm] node_scaled_dot_product_attention_7_out_mm_out = MatMul (scaled_dot_product_attention_7, node_scaled_dot_product_attention_7_wo_t)
   [node_scaled_dot_product_attention_7_out_bias] node_scaled_dot_product_attention_7_out = Add (node_scaled_dot_product_attention_7_out_mm_out, "visual.transformer.resblocks.7.attn.out_proj.bias")
   add_1088 = Add (add_973, node_scaled_dot_product_attention_7_out)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1088, "visual.transformer.resblocks.7.ln_2.weight", "visual.transformer.resblocks.7.ln_2.bias")
   val_58 = MatMul (layer_norm_16, val_28)
   linear_30 = Add (val_58, "visual.transformer.resblocks.7.mlp.c_fc.bias")
   gelu_7 = Gelu <approximate: string = "none"> (linear_30)
   val_59 = MatMul (gelu_7, val_29)
   linear_31 = Add (val_59, "visual.transformer.resblocks.7.mlp.c_proj.bias")
   add_1109 = Add (add_1088, linear_31)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1109, "visual.transformer.resblocks.8.ln_1.weight", "visual.transformer.resblocks.8.ln_1.bias")
   [node_scaled_dot_product_attention_8_qkv_mm] node_scaled_dot_product_attention_8_qkv_mm_out = MatMul (layer_norm_17, val_30)
   [node_scaled_dot_product_attention_8_qkv_bias] node_scaled_dot_product_attention_8_qkv = Add (node_scaled_dot_product_attention_8_qkv_mm_out, "visual.transformer.resblocks.8.attn.in_proj_bias")
   [node_scaled_dot_product_attention_8_qkv_split] node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v = Split <axis: int = -1> (node_scaled_dot_product_attention_8_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_8_q, node_scaled_dot_product_attention_8_k, node_scaled_dot_product_attention_8_v)
   [node_scaled_dot_product_attention_8_out_mm] node_scaled_dot_product_attention_8_out_mm_out = MatMul (scaled_dot_product_attention_8, node_scaled_dot_product_attention_8_wo_t)
   [node_scaled_dot_product_attention_8_out_bias] node_scaled_dot_product_attention_8_out = Add (node_scaled_dot_product_attention_8_out_mm_out, "visual.transformer.resblocks.8.attn.out_proj.bias")
   add_1224 = Add (add_1109, node_scaled_dot_product_attention_8_out)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1224, "visual.transformer.resblocks.8.ln_2.weight", "visual.transformer.resblocks.8.ln_2.bias")
   val_60 = MatMul (layer_norm_18, val_31)
   linear_34 = Add (val_60, "visual.transformer.resblocks.8.mlp.c_fc.bias")
   gelu_8 = Gelu <approximate: string = "none"> (linear_34)
   val_61 = MatMul (gelu_8, val_32)
   linear_35 = Add (val_61, "visual.transformer.resblocks.8.mlp.c_proj.bias")
   add_1245 = Add (add_1224, linear_35)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1245, "visual.transformer.resblocks.9.ln_1.weight", "visual.transformer.resblocks.9.ln_1.bias")
   [node_scaled_dot_product_attention_9_qkv_mm] node_scaled_dot_product_attention_9_qkv_mm_out = MatMul (layer_norm_19, val_33)
   [node_scaled_dot_product_attention_9_qkv_bias] node_scaled_dot_product_attention_9_qkv = Add (node_scaled_dot_product_attention_9_qkv_mm_out, "visual.transformer.resblocks.9.attn.in_proj_bias")
   [node_scaled_dot_product_attention_9_qkv_split] node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v = Split <axis: int = -1> (node_scaled_dot_product_attention_9_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_9_q, node_scaled_dot_product_attention_9_k, node_scaled_dot_product_attention_9_v)
   [node_scaled_dot_product_attention_9_out_mm] node_scaled_dot_product_attention_9_out_mm_out = MatMul (scaled_dot_product_attention_9, node_scaled_dot_product_attention_9_wo_t)
   [node_scaled_dot_product_attention_9_out_bias] node_scaled_dot_product_attention_9_out = Add (node_scaled_dot_product_attention_9_out_mm_out, "visual.transformer.resblocks.9.attn.out_proj.bias")
   add_1360 = Add (add_1245, node_scaled_dot_product_attention_9_out)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1360, "visual.transformer.resblocks.9.ln_2.weight", "visual.transformer.resblocks.9.ln_2.bias")
   val_62 = MatMul (layer_norm_20, val_34)
   linear_38 = Add (val_62, "visual.transformer.resblocks.9.mlp.c_fc.bias")
   gelu_9 = Gelu <approximate: string = "none"> (linear_38)
   val_63 = MatMul (gelu_9, val_35)
   linear_39 = Add (val_63, "visual.transformer.resblocks.9.mlp.c_proj.bias")
   add_1381 = Add (add_1360, linear_39)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1381, "visual.transformer.resblocks.10.ln_1.weight", "visual.transformer.resblocks.10.ln_1.bias")
   [node_scaled_dot_product_attention_10_qkv_mm] node_scaled_dot_product_attention_10_qkv_mm_out = MatMul (layer_norm_21, val_36)
   [node_scaled_dot_product_attention_10_qkv_bias] node_scaled_dot_product_attention_10_qkv = Add (node_scaled_dot_product_attention_10_qkv_mm_out, "visual.transformer.resblocks.10.attn.in_proj_bias")
   [node_scaled_dot_product_attention_10_qkv_split] node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v = Split <axis: int = -1> (node_scaled_dot_product_attention_10_qkv, attn3d_split_3x768)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_10_q, node_scaled_dot_product_attention_10_k, node_scaled_dot_product_attention_10_v)
   [node_scaled_dot_product_attention_10_out_mm] node_scaled_dot_product_attention_10_out_mm_out = MatMul (scaled_dot_product_attention_10, node_scaled_dot_product_attention_10_wo_t)
   [node_scaled_dot_product_attention_10_out_bias] node_scaled_dot_product_attention_10_out = Add (node_scaled_dot_product_attention_10_out_mm_out, "visual.transformer.resblocks.10.attn.out_proj.bias")
   add_1496 = Add (add_1381, node_scaled_dot_product_attention_10_out)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1496, "visual.transformer.resblocks.10.ln_2.weight", "visual.transformer.resblocks.10.ln_2.bias")
   val_64 = MatMul (layer_norm_22, val_37)
   linear_42 = Add (val_64, "visual.transformer.resblocks.10.mlp.c_fc.bias")
   gelu_10 = Gelu <approximate: string = "none"> (linear_42)
   val_65 = MatMul (gelu_10, val_38)
   linear_43 = Add (val_65, "visual.transformer.resblocks.10.mlp.c_proj.bias")
   add_1517 = Add (add_1496, linear_43)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1517, "visual.transformer.resblocks.11.ln_1.weight", "visual.transformer.resblocks.11.ln_1.bias")
   [node_scaled_dot_product_attention_11_qkv_mm] node_scaled_dot_product_attention_11_qkv_mm_out = MatMul (layer_norm_23, val_39)
   [node_scaled_dot_product_attention_11_qkv_bias] node_scaled_dot_product_attention_11_qkv = Add (node_scaled_dot_product_attention_11_qkv_mm_out, "visual.transformer.resblocks.11.attn.in_proj_bias")
   [node_scaled_dot_product_attention_11_qkv_split] node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v = Split <axis: int = -1> (node_scaled_dot_product_attention_11_qkv, attn3d_split_3x768)
   [pool_hoist_node_scaled_dot_product_attention_11_q] node_scaled_dot_product_attention_11_q_pooled = Slice (node_scaled_dot_product_attention_11_q, val_2, val_5, val_5)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 0, kv_num_heads: int = 12, q_num_heads: int = 12, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_11_q_pooled, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v)
   [node_scaled_dot_product_attention_11_out_mm] node_scaled_dot_product_attention_11_out_mm_out = MatMul (scaled_dot_product_attention_11, node_scaled_dot_product_attention_11_wo_t)
   [node_scaled_dot_product_attention_11_out_bias] node_scaled_dot_product_attention_11_out = Add (node_scaled_dot_product_attention_11_out_mm_out, "visual.transformer.resblocks.11.attn.out_proj.bias")
   [pool_hoist_add_1517] add_1517_pooled = Slice (add_1517, val_2, val_5, val_5)
   add_1632 = Add (add_1517_pooled, node_scaled_dot_product_attention_11_out)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1632, "visual.transformer.resblocks.11.ln_2.weight", "visual.transformer.resblocks.11.ln_2.bias")
   val_66 = MatMul (layer_norm_24, val_40)
   linear_46 = Add (val_66, "visual.transformer.resblocks.11.mlp.c_fc.bias")
   gelu_11 = Gelu <approximate: string = "none"> (linear_46)
   val_67 = MatMul (gelu_11, val_41)
   linear_47 = Add (val_67, "visual.transformer.resblocks.11.mlp.c_proj.bias")
   add_1653 = Add (add_1632, linear_47)
   val_68 = Squeeze (add_1653, val_5)
   select_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (val_68, "visual.ln_post.weight", "visual.ln_post.bias")
   [node_matmul] matmul = MatMul (select_36, "visual.proj")
   [node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (matmul, val_0)
   [node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_1)
   [node_div] image_embedding = Div (matmul, clamp_min)
}

weights:
attn3d_split_3x768 INT64[3] eca50b2daf34
node_Conv_704_fused_bias FLOAT[768] 799a25158bb7
node_scaled_dot_product_attention_10_wo_t FLOAT[768,768] bf9eef022b15
node_scaled_dot_product_attention_11_wo_t FLOAT[768,768] 231143b9a626
node_scaled_dot_product_attention_1_wo_t FLOAT[768,768] a27e6288a7e0
node_scaled_dot_product_attention_2_wo_t FLOAT[768,768] 9e3190e6b459
node_scaled_dot_product_attention_3_wo_t FLOAT[768,768] 318582ea544e
node_scaled_dot_product_attention_4_wo_t FLOAT[768,768] e1304858071f
node_scaled_dot_product_attention_5_wo_t FLOAT[768,768] e8b5102608d2
node_scaled_dot_product_attention_6_wo_t FLOAT[768,768] 1593fb3f0bdf
node_scaled_dot_product_attention_7_wo_t FLOAT[768,768] 5c85df03d917
node_scaled_dot_product_attention_8_wo_t FLOAT[768,768] 07205dff3c3f
node_scaled_dot_product_attention_9_wo_t FLOAT[768,768] c6d2159ac451
node_scaled_dot_product_attention_wo_t FLOAT[768,768] d298dea248ba
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[768,3072] 9a765b083fc0
val_11 FLOAT[3072,768] eec977effed7
val_12 FLOAT[768,2304] 8c678c126da4
val_13 FLOAT[768,3072] 9c3780d937d4
val_14 FLOAT[3072,768] ab9f68b0ac5e
val_15 FLOAT[768,2304] c34595af10f2
val_16 FLOAT[768,3072] fc935bc9bc7c
val_17 FLOAT[3072,768] 825212361feb
val_18 FLOAT[768,2304] 274b4b571693
val_19 FLOAT[768,3072] 98873f99c7e5
val_2 INT64[1] af5570f5a181
val_20 FLOAT[3072,768] d93e9ac834fe
val_21 FLOAT[768,2304] 43896aacf7c6
val_22 FLOAT[768,3072] d405e6afdc40
val_23 FLOAT[3072,768] eb5a13664a10
val_24 FLOAT[768,2304] 1c0ceb5514ea
val_25 FLOAT[768,3072] 41d7ca48c916
val_26 FLOAT[3072,768] 33c2465329c5
val_27 FLOAT[768,2304] 9741b3461247
val_28 FLOAT[768,3072] 18939c4e36e9
val_29 FLOAT[3072,768] 1cfd1b2d91b8
val_3 INT64[6] 6b7d92eaae70
val_30 FLOAT[768,2304] 2e78aba72932
val_31 FLOAT[768,3072] 466519ef6d75
val_32 FLOAT[3072,768] 7f1140450ca7
val_33 FLOAT[768,2304] efb258d47c36
val_34 FLOAT[768,3072] e89f4942cb0b
val_35 FLOAT[3072,768] 379f57966677
val_36 FLOAT[768,2304] 8746e4da713e
val_37 FLOAT[768,3072] 66300aab7917
val_38 FLOAT[3072,768] e6cf84c991cd
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visual.transformer.resblocks.11.mlp.c_fc.bias FLOAT[3072] 2e9bb1848703
visual.transformer.resblocks.11.mlp.c_proj.bias FLOAT[768] 8af895689cc2
visual.transformer.resblocks.2.attn.in_proj_bias FLOAT[2304] 201e25275016
visual.transformer.resblocks.2.attn.out_proj.bias FLOAT[768] 96fc48d21053
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visual.transformer.resblocks.2.ln_2.bias FLOAT[768] 41ec11c4433f
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visual.transformer.resblocks.3.attn.out_proj.bias FLOAT[768] 2e997f9c972e
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visual.transformer.resblocks.3.mlp.c_proj.bias FLOAT[768] eb3f0256c433
visual.transformer.resblocks.4.attn.in_proj_bias FLOAT[2304] a248bde02047
visual.transformer.resblocks.4.attn.out_proj.bias FLOAT[768] 1d0340604676
visual.transformer.resblocks.4.ln_1.bias FLOAT[768] 4f9029592c3f
visual.transformer.resblocks.4.ln_1.weight FLOAT[768] 45cca5ea733e
visual.transformer.resblocks.4.ln_2.bias FLOAT[768] fb42844a2277
visual.transformer.resblocks.4.ln_2.weight FLOAT[768] 249a1d8b6d86
visual.transformer.resblocks.4.mlp.c_fc.bias FLOAT[3072] b2af754fb52e
visual.transformer.resblocks.4.mlp.c_proj.bias FLOAT[768] 8d14f227a87d
visual.transformer.resblocks.5.attn.in_proj_bias FLOAT[2304] 9b8d2f922b34
visual.transformer.resblocks.5.attn.out_proj.bias FLOAT[768] 65ba214f673e
visual.transformer.resblocks.5.ln_1.bias FLOAT[768] f7ef5d3562d0
visual.transformer.resblocks.5.ln_1.weight FLOAT[768] 4e2ccb0a165f
visual.transformer.resblocks.5.ln_2.bias FLOAT[768] e80ab11bb94b
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visual.transformer.resblocks.5.mlp.c_fc.bias FLOAT[3072] 16331a1fad46
visual.transformer.resblocks.5.mlp.c_proj.bias FLOAT[768] 088dcc34f8b1
visual.transformer.resblocks.6.attn.in_proj_bias FLOAT[2304] 4e602c091077
visual.transformer.resblocks.6.attn.out_proj.bias FLOAT[768] e0a4fa9ec557
visual.transformer.resblocks.6.ln_1.bias FLOAT[768] 2e7cf53abb77
visual.transformer.resblocks.6.ln_1.weight FLOAT[768] 8275394be666
visual.transformer.resblocks.6.ln_2.bias FLOAT[768] 4fcce19797d3
visual.transformer.resblocks.6.ln_2.weight FLOAT[768] 918bcb8c5d62
visual.transformer.resblocks.6.mlp.c_fc.bias FLOAT[3072] 890d9d9cf123
visual.transformer.resblocks.6.mlp.c_proj.bias FLOAT[768] 56a117bbf3d0
visual.transformer.resblocks.7.attn.in_proj_bias FLOAT[2304] 83512e372f10
visual.transformer.resblocks.7.attn.out_proj.bias FLOAT[768] 49320fe551a8
visual.transformer.resblocks.7.ln_1.bias FLOAT[768] d8c82cbe070b
visual.transformer.resblocks.7.ln_1.weight FLOAT[768] 1de5c2e2627f
visual.transformer.resblocks.7.ln_2.bias FLOAT[768] 1312bbf94c4f
visual.transformer.resblocks.7.ln_2.weight FLOAT[768] f52f5d7a3bcb
visual.transformer.resblocks.7.mlp.c_fc.bias FLOAT[3072] 417a9b3afe3b
visual.transformer.resblocks.7.mlp.c_proj.bias FLOAT[768] 076cd0df0c53
visual.transformer.resblocks.8.attn.in_proj_bias FLOAT[2304] a49258034d93
visual.transformer.resblocks.8.attn.out_proj.bias FLOAT[768] a5770708f258
visual.transformer.resblocks.8.ln_1.bias FLOAT[768] 01eb4d3d765b
visual.transformer.resblocks.8.ln_1.weight FLOAT[768] e005cc2b217e
visual.transformer.resblocks.8.ln_2.bias FLOAT[768] 3c8a44b7b95e
visual.transformer.resblocks.8.ln_2.weight FLOAT[768] cca46ff64772
visual.transformer.resblocks.8.mlp.c_fc.bias FLOAT[3072] d475ef9a80c6
visual.transformer.resblocks.8.mlp.c_proj.bias FLOAT[768] c6ddcff89448
visual.transformer.resblocks.9.attn.in_proj_bias FLOAT[2304] 063cacdf7556
visual.transformer.resblocks.9.attn.out_proj.bias FLOAT[768] 6d43adcd0178
visual.transformer.resblocks.9.ln_1.bias FLOAT[768] 837f444fbd8e
visual.transformer.resblocks.9.ln_1.weight FLOAT[768] 86e0a9039686
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visual.transformer.resblocks.9.mlp.c_proj.bias FLOAT[768] b77004fe1146
