<
   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_1000
      float[batch,50,768] add_1029
      float[batch,50,768] add_1144
      float[batch,50,768] add_1173
      float[batch,50,768] add_1288
      float[batch,50,768] add_1317
      float[batch,50,768] add_136
      float[batch,50,768] add_1432
      float[batch,50,768] add_1461
      float[batch,50,768] add_1576
      float[batch,50,768] add_1605
      float[batch,1,768] add_1605_pooled
      float[batch,50,768] add_165
      float[batch,50,768] add_17
      float[batch,1,768] add_1720
      float[batch,1,768] add_1749
      float[batch,50,768] add_280
      float[batch,50,768] add_309
      float[batch,50,768] add_424
      float[batch,50,768] add_453
      float[batch,50,768] add_568
      float[batch,50,768] add_597
      float[batch,50,768] add_712
      float[batch,50,768] add_741
      float[batch,50,768] add_856
      float[batch,50,768] add_885
      float[batch,1] clamp_min
      float[batch,768,7,7] conv2d
      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,50,3072] mul_1078
      float[batch,50,3072] mul_1083
      float[batch,50,3072] mul_115
      float[batch,50,3072] mul_1185
      float[batch,50,3072] mul_1190
      float[batch,50,3072] mul_120
      float[batch,1,3072] mul_1292
      float[batch,1,3072] mul_1297
      float[batch,50,3072] mul_222
      float[batch,50,3072] mul_227
      float[batch,50,3072] mul_329
      float[batch,50,3072] mul_334
      float[batch,50,3072] mul_436
      float[batch,50,3072] mul_441
      float[batch,50,3072] mul_543
      float[batch,50,3072] mul_548
      float[batch,50,3072] mul_650
      float[batch,50,3072] mul_655
      float[batch,50,3072] mul_757
      float[batch,50,3072] mul_762
      float[batch,50,3072] mul_864
      float[batch,50,3072] mul_869
      float[batch,50,3072] mul_971
      float[batch,50,3072] mul_976
      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,50,3072] sigmoid
      float[batch,50,3072] sigmoid_1
      float[batch,50,3072] sigmoid_10
      float[batch,1,3072] sigmoid_11
      float[batch,50,3072] sigmoid_2
      float[batch,50,3072] sigmoid_3
      float[batch,50,3072] sigmoid_4
      float[batch,50,3072] sigmoid_5
      float[batch,50,3072] sigmoid_6
      float[batch,50,3072] sigmoid_7
      float[batch,50,3072] sigmoid_8
      float[batch,50,3072] sigmoid_9
      float[batch,50,768] val_44
      float[batch,50,3072] val_45
      float[batch,50,768] val_46
      float[batch,50,3072] val_47
      float[batch,50,768] val_48
      float[batch,50,3072] val_49
      float[batch,50,768] val_50
      float[batch,50,3072] val_51
      float[batch,50,768] val_52
      float[batch,50,3072] val_53
      float[batch,50,768] val_54
      float[batch,50,3072] val_55
      float[batch,50,768] val_56
      float[batch,50,3072] val_57
      float[batch,50,768] val_58
      float[batch,50,3072] val_59
      float[batch,50,768] val_60
      float[batch,50,3072] val_61
      float[batch,50,768] val_62
      float[batch,50,3072] val_63
      float[batch,50,768] val_64
      float[batch,50,3072] val_65
      float[batch,50,768] val_66
      float[batch,1,3072] val_67
      float[batch,1,768] val_68
      float[batch,768] val_69
      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_705_fused_bias)
   view = Reshape <allowzero: int = 1> (conv2d, view_target)
   [node_permute] permute = Transpose <perm: ints = [0, 2, 1]> (view)
   val_44 = Pad (permute, val_4, val_5)
   add_17 = Add (val_44, val_43)
   [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_7)
   [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_45 = MatMul (layer_norm_2, val_8)
   linear_2 = Add (val_45, "visual.transformer.resblocks.0.mlp.c_fc.bias")
   mul_115 = Mul (linear_2, val_3)
   [node_sigmoid] sigmoid = Sigmoid (mul_115)
   mul_120 = Mul (linear_2, sigmoid)
   val_46 = MatMul (mul_120, val_9)
   linear_3 = Add (val_46, "visual.transformer.resblocks.0.mlp.c_proj.bias")
   add_165 = Add (add_136, linear_3)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_165, "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_10)
   [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_280 = Add (add_165, node_scaled_dot_product_attention_1_out)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_280, "visual.transformer.resblocks.1.ln_2.weight", "visual.transformer.resblocks.1.ln_2.bias")
   val_47 = MatMul (layer_norm_4, val_11)
   linear_6 = Add (val_47, "visual.transformer.resblocks.1.mlp.c_fc.bias")
   mul_222 = Mul (linear_6, val_3)
   sigmoid_1 = Sigmoid (mul_222)
   mul_227 = Mul (linear_6, sigmoid_1)
   val_48 = MatMul (mul_227, val_12)
   linear_7 = Add (val_48, "visual.transformer.resblocks.1.mlp.c_proj.bias")
   add_309 = Add (add_280, linear_7)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_309, "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_13)
   [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_424 = Add (add_309, node_scaled_dot_product_attention_2_out)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_424, "visual.transformer.resblocks.2.ln_2.weight", "visual.transformer.resblocks.2.ln_2.bias")
   val_49 = MatMul (layer_norm_6, val_14)
   linear_10 = Add (val_49, "visual.transformer.resblocks.2.mlp.c_fc.bias")
   mul_329 = Mul (linear_10, val_3)
   sigmoid_2 = Sigmoid (mul_329)
   mul_334 = Mul (linear_10, sigmoid_2)
   val_50 = MatMul (mul_334, val_15)
   linear_11 = Add (val_50, "visual.transformer.resblocks.2.mlp.c_proj.bias")
   add_453 = Add (add_424, linear_11)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_453, "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_16)
   [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_568 = Add (add_453, node_scaled_dot_product_attention_3_out)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_568, "visual.transformer.resblocks.3.ln_2.weight", "visual.transformer.resblocks.3.ln_2.bias")
   val_51 = MatMul (layer_norm_8, val_17)
   linear_14 = Add (val_51, "visual.transformer.resblocks.3.mlp.c_fc.bias")
   mul_436 = Mul (linear_14, val_3)
   sigmoid_3 = Sigmoid (mul_436)
   mul_441 = Mul (linear_14, sigmoid_3)
   val_52 = MatMul (mul_441, val_18)
   linear_15 = Add (val_52, "visual.transformer.resblocks.3.mlp.c_proj.bias")
   add_597 = Add (add_568, linear_15)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_597, "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_19)
   [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_712 = Add (add_597, node_scaled_dot_product_attention_4_out)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_712, "visual.transformer.resblocks.4.ln_2.weight", "visual.transformer.resblocks.4.ln_2.bias")
   val_53 = MatMul (layer_norm_10, val_20)
   linear_18 = Add (val_53, "visual.transformer.resblocks.4.mlp.c_fc.bias")
   mul_543 = Mul (linear_18, val_3)
   sigmoid_4 = Sigmoid (mul_543)
   mul_548 = Mul (linear_18, sigmoid_4)
   val_54 = MatMul (mul_548, val_21)
   linear_19 = Add (val_54, "visual.transformer.resblocks.4.mlp.c_proj.bias")
   add_741 = Add (add_712, linear_19)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_741, "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_22)
   [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_856 = Add (add_741, node_scaled_dot_product_attention_5_out)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_856, "visual.transformer.resblocks.5.ln_2.weight", "visual.transformer.resblocks.5.ln_2.bias")
   val_55 = MatMul (layer_norm_12, val_23)
   linear_22 = Add (val_55, "visual.transformer.resblocks.5.mlp.c_fc.bias")
   mul_650 = Mul (linear_22, val_3)
   sigmoid_5 = Sigmoid (mul_650)
   mul_655 = Mul (linear_22, sigmoid_5)
   val_56 = MatMul (mul_655, val_24)
   linear_23 = Add (val_56, "visual.transformer.resblocks.5.mlp.c_proj.bias")
   add_885 = Add (add_856, linear_23)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_885, "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_25)
   [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_1000 = Add (add_885, node_scaled_dot_product_attention_6_out)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1000, "visual.transformer.resblocks.6.ln_2.weight", "visual.transformer.resblocks.6.ln_2.bias")
   val_57 = MatMul (layer_norm_14, val_26)
   linear_26 = Add (val_57, "visual.transformer.resblocks.6.mlp.c_fc.bias")
   mul_757 = Mul (linear_26, val_3)
   sigmoid_6 = Sigmoid (mul_757)
   mul_762 = Mul (linear_26, sigmoid_6)
   val_58 = MatMul (mul_762, val_27)
   linear_27 = Add (val_58, "visual.transformer.resblocks.6.mlp.c_proj.bias")
   add_1029 = Add (add_1000, linear_27)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1029, "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_28)
   [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_1144 = Add (add_1029, node_scaled_dot_product_attention_7_out)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1144, "visual.transformer.resblocks.7.ln_2.weight", "visual.transformer.resblocks.7.ln_2.bias")
   val_59 = MatMul (layer_norm_16, val_29)
   linear_30 = Add (val_59, "visual.transformer.resblocks.7.mlp.c_fc.bias")
   mul_864 = Mul (linear_30, val_3)
   sigmoid_7 = Sigmoid (mul_864)
   mul_869 = Mul (linear_30, sigmoid_7)
   val_60 = MatMul (mul_869, val_30)
   linear_31 = Add (val_60, "visual.transformer.resblocks.7.mlp.c_proj.bias")
   add_1173 = Add (add_1144, linear_31)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1173, "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_31)
   [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_1288 = Add (add_1173, node_scaled_dot_product_attention_8_out)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1288, "visual.transformer.resblocks.8.ln_2.weight", "visual.transformer.resblocks.8.ln_2.bias")
   val_61 = MatMul (layer_norm_18, val_32)
   linear_34 = Add (val_61, "visual.transformer.resblocks.8.mlp.c_fc.bias")
   mul_971 = Mul (linear_34, val_3)
   sigmoid_8 = Sigmoid (mul_971)
   mul_976 = Mul (linear_34, sigmoid_8)
   val_62 = MatMul (mul_976, val_33)
   linear_35 = Add (val_62, "visual.transformer.resblocks.8.mlp.c_proj.bias")
   add_1317 = Add (add_1288, linear_35)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1317, "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_34)
   [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_1432 = Add (add_1317, node_scaled_dot_product_attention_9_out)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1432, "visual.transformer.resblocks.9.ln_2.weight", "visual.transformer.resblocks.9.ln_2.bias")
   val_63 = MatMul (layer_norm_20, val_35)
   linear_38 = Add (val_63, "visual.transformer.resblocks.9.mlp.c_fc.bias")
   mul_1078 = Mul (linear_38, val_3)
   sigmoid_9 = Sigmoid (mul_1078)
   mul_1083 = Mul (linear_38, sigmoid_9)
   val_64 = MatMul (mul_1083, val_36)
   linear_39 = Add (val_64, "visual.transformer.resblocks.9.mlp.c_proj.bias")
   add_1461 = Add (add_1432, linear_39)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1461, "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_37)
   [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_1576 = Add (add_1461, node_scaled_dot_product_attention_10_out)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1576, "visual.transformer.resblocks.10.ln_2.weight", "visual.transformer.resblocks.10.ln_2.bias")
   val_65 = MatMul (layer_norm_22, val_38)
   linear_42 = Add (val_65, "visual.transformer.resblocks.10.mlp.c_fc.bias")
   mul_1185 = Mul (linear_42, val_3)
   sigmoid_10 = Sigmoid (mul_1185)
   mul_1190 = Mul (linear_42, sigmoid_10)
   val_66 = MatMul (mul_1190, val_39)
   linear_43 = Add (val_66, "visual.transformer.resblocks.10.mlp.c_proj.bias")
   add_1605 = Add (add_1576, linear_43)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1605, "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_40)
   [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_6, val_6)
   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_1605] add_1605_pooled = Slice (add_1605, val_2, val_6, val_6)
   add_1720 = Add (add_1605_pooled, node_scaled_dot_product_attention_11_out)
   layer_norm_24 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1720, "visual.transformer.resblocks.11.ln_2.weight", "visual.transformer.resblocks.11.ln_2.bias")
   val_67 = MatMul (layer_norm_24, val_41)
   linear_46 = Add (val_67, "visual.transformer.resblocks.11.mlp.c_fc.bias")
   mul_1292 = Mul (linear_46, val_3)
   sigmoid_11 = Sigmoid (mul_1292)
   mul_1297 = Mul (linear_46, sigmoid_11)
   val_68 = MatMul (mul_1297, val_42)
   linear_47 = Add (val_68, "visual.transformer.resblocks.11.mlp.c_proj.bias")
   add_1749 = Add (add_1720, linear_47)
   val_69 = Squeeze (add_1749, val_6)
   select_36 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (val_69, "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_705_fused_bias FLOAT[768] 3f1fdeba0a96
node_scaled_dot_product_attention_10_wo_t FLOAT[768,768] b37ec39306ec
node_scaled_dot_product_attention_11_wo_t FLOAT[768,768] 55015c54b68e
node_scaled_dot_product_attention_1_wo_t FLOAT[768,768] 6dc6738d93d1
node_scaled_dot_product_attention_2_wo_t FLOAT[768,768] 9525e2172347
node_scaled_dot_product_attention_3_wo_t FLOAT[768,768] ce578205e776
node_scaled_dot_product_attention_4_wo_t FLOAT[768,768] 0e5e67651d0a
node_scaled_dot_product_attention_5_wo_t FLOAT[768,768] f54176cf5e80
node_scaled_dot_product_attention_6_wo_t FLOAT[768,768] 7778114695dc
node_scaled_dot_product_attention_7_wo_t FLOAT[768,768] 59d3e42916a6
node_scaled_dot_product_attention_8_wo_t FLOAT[768,768] 5ff8780a14c8
node_scaled_dot_product_attention_9_wo_t FLOAT[768,768] 269a0b0102c1
node_scaled_dot_product_attention_wo_t FLOAT[768,768] 2fa1267ea4b1
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[768,2304] f5e6ed04c6b4
val_11 FLOAT[768,3072] 1d8e960ece36
val_12 FLOAT[3072,768] 7ca34bb6184c
val_13 FLOAT[768,2304] 41d9239d0b23
val_14 FLOAT[768,3072] 0db8e992e631
val_15 FLOAT[3072,768] 16f5c0a3f37d
val_16 FLOAT[768,2304] 8b0e4a013e3c
val_17 FLOAT[768,3072] 49313db4e68e
val_18 FLOAT[3072,768] 10c545909ff6
val_19 FLOAT[768,2304] 9d0de5cb5be8
val_2 INT64[1] af5570f5a181
val_20 FLOAT[768,3072] be60f587cf2a
val_21 FLOAT[3072,768] 125cf7f097c3
val_22 FLOAT[768,2304] e4019e47b282
val_23 FLOAT[768,3072] 7392bf6e3aca
val_24 FLOAT[3072,768] 47a868b95489
val_25 FLOAT[768,2304] 3c6e3968b8b8
val_26 FLOAT[768,3072] c84d968cac19
val_27 FLOAT[3072,768] 1a73d442cc31
val_28 FLOAT[768,2304] 18f133983a9e
val_29 FLOAT[768,3072] 4e575ad9e4a4
val_3 FLOAT[] c2e7ddfe3114
val_30 FLOAT[3072,768] 5d105e70fd25
val_31 FLOAT[768,2304] 7394f700b270
val_32 FLOAT[768,3072] af9454e7e72c
val_33 FLOAT[3072,768] 0384196cdf7d
val_34 FLOAT[768,2304] 1b9592e0690f
val_35 FLOAT[768,3072] 94e913e280cd
val_36 FLOAT[3072,768] 8e65cfabc8b6
val_37 FLOAT[768,2304] 1ad1732e6519
val_38 FLOAT[768,3072] 0b52afc4984a
val_39 FLOAT[3072,768] 8365128ed631
val_4 INT64[6] 6b7d92eaae70
val_40 FLOAT[768,2304] 96246d08fb55
val_41 FLOAT[768,3072] 8e903896ac57
val_42 FLOAT[3072,768] c46a0ba00fc5
val_43 FLOAT[1,50,768] 17773a51fc5d
val_5 FLOAT[] df3f619804a9
val_6 INT64[1] 7c9fa136d441
val_7 FLOAT[768,2304] 7d0a7ceaffb7
val_8 FLOAT[768,3072] f5def8fd12f5
val_9 FLOAT[3072,768] a3017fb78784
view_target INT64[3] 69b00f163d22
visual.conv1.weight FLOAT[768,3,32,32] ae6c9eaa401d
visual.ln_post.bias FLOAT[768] 52148d3ca662
visual.ln_post.weight FLOAT[768] 7852fb82fbeb
visual.ln_pre.bias FLOAT[768] 21345b0509de
visual.ln_pre.weight FLOAT[768] 588459d11d9f
visual.proj FLOAT[768,512] 5fc358a09847
visual.transformer.resblocks.0.attn.in_proj_bias FLOAT[2304] 0673b9d3faa0
visual.transformer.resblocks.0.attn.out_proj.bias FLOAT[768] 45bcb15a04ed
visual.transformer.resblocks.0.ln_1.bias FLOAT[768] 4b15737435c4
visual.transformer.resblocks.0.ln_1.weight FLOAT[768] 4d2d4f55c64c
visual.transformer.resblocks.0.ln_2.bias FLOAT[768] 405d8b0bf353
visual.transformer.resblocks.0.ln_2.weight FLOAT[768] eb1585c759b1
visual.transformer.resblocks.0.mlp.c_fc.bias FLOAT[3072] 9c9632c42092
visual.transformer.resblocks.0.mlp.c_proj.bias FLOAT[768] f58e7734d8aa
visual.transformer.resblocks.1.attn.in_proj_bias FLOAT[2304] 872eb7416b61
visual.transformer.resblocks.1.attn.out_proj.bias FLOAT[768] 15d5fa8692ba
visual.transformer.resblocks.1.ln_1.bias FLOAT[768] 517c1c1dc496
visual.transformer.resblocks.1.ln_1.weight FLOAT[768] 4bb7567067d2
visual.transformer.resblocks.1.ln_2.bias FLOAT[768] ff7378e17797
visual.transformer.resblocks.1.ln_2.weight FLOAT[768] dfc4338f11ef
visual.transformer.resblocks.1.mlp.c_fc.bias FLOAT[3072] 6dbac278fff4
visual.transformer.resblocks.1.mlp.c_proj.bias FLOAT[768] 0a1dde6dd16e
visual.transformer.resblocks.10.attn.in_proj_bias FLOAT[2304] 9ad1108c0595
visual.transformer.resblocks.10.attn.out_proj.bias FLOAT[768] f090b0d13061
visual.transformer.resblocks.10.ln_1.bias FLOAT[768] 5fe9c7ee5cc3
visual.transformer.resblocks.10.ln_1.weight FLOAT[768] faf736142587
visual.transformer.resblocks.10.ln_2.bias FLOAT[768] ba49b1131419
visual.transformer.resblocks.10.ln_2.weight FLOAT[768] 284af60b3c25
visual.transformer.resblocks.10.mlp.c_fc.bias FLOAT[3072] 88503516c49a
visual.transformer.resblocks.10.mlp.c_proj.bias FLOAT[768] a6ef2e5550bc
visual.transformer.resblocks.11.attn.in_proj_bias FLOAT[2304] c92d8ccdc4b2
visual.transformer.resblocks.11.attn.out_proj.bias FLOAT[768] e66141f1ab4e
visual.transformer.resblocks.11.ln_1.bias FLOAT[768] 282faa36d1b6
visual.transformer.resblocks.11.ln_1.weight FLOAT[768] c2e1ccab04a3
visual.transformer.resblocks.11.ln_2.bias FLOAT[768] 9e09e313cfb3
visual.transformer.resblocks.11.ln_2.weight FLOAT[768] 39c54590d157
visual.transformer.resblocks.11.mlp.c_fc.bias FLOAT[3072] 7128eec1da8a
visual.transformer.resblocks.11.mlp.c_proj.bias FLOAT[768] a7f37999e897
visual.transformer.resblocks.2.attn.in_proj_bias FLOAT[2304] 54d82746844f
visual.transformer.resblocks.2.attn.out_proj.bias FLOAT[768] 6fbabaf171e9
visual.transformer.resblocks.2.ln_1.bias FLOAT[768] dc532e38cd35
visual.transformer.resblocks.2.ln_1.weight FLOAT[768] 6f33f49122dd
visual.transformer.resblocks.2.ln_2.bias FLOAT[768] c524e18aa280
visual.transformer.resblocks.2.ln_2.weight FLOAT[768] 9e036e4e8200
visual.transformer.resblocks.2.mlp.c_fc.bias FLOAT[3072] 87eb79e29201
visual.transformer.resblocks.2.mlp.c_proj.bias FLOAT[768] 10ce1952da4a
visual.transformer.resblocks.3.attn.in_proj_bias FLOAT[2304] e1c1d3cc540b
visual.transformer.resblocks.3.attn.out_proj.bias FLOAT[768] acbbe91195f9
visual.transformer.resblocks.3.ln_1.bias FLOAT[768] ec3b6555f2f2
visual.transformer.resblocks.3.ln_1.weight FLOAT[768] a3c490154ca6
visual.transformer.resblocks.3.ln_2.bias FLOAT[768] 4a49315bd052
visual.transformer.resblocks.3.ln_2.weight FLOAT[768] 3290c7a2deec
visual.transformer.resblocks.3.mlp.c_fc.bias FLOAT[3072] 2820d405fa93
visual.transformer.resblocks.3.mlp.c_proj.bias FLOAT[768] 03440c04e911
visual.transformer.resblocks.4.attn.in_proj_bias FLOAT[2304] 8048b9f7e128
visual.transformer.resblocks.4.attn.out_proj.bias FLOAT[768] fd8e8b530083
visual.transformer.resblocks.4.ln_1.bias FLOAT[768] 5b3c545a7841
visual.transformer.resblocks.4.ln_1.weight FLOAT[768] 28880a85ce93
visual.transformer.resblocks.4.ln_2.bias FLOAT[768] b21364cd3224
visual.transformer.resblocks.4.ln_2.weight FLOAT[768] 6f9fa776276d
visual.transformer.resblocks.4.mlp.c_fc.bias FLOAT[3072] d8d4c705dfa6
visual.transformer.resblocks.4.mlp.c_proj.bias FLOAT[768] 486a8069c1dd
visual.transformer.resblocks.5.attn.in_proj_bias FLOAT[2304] 35d304845651
visual.transformer.resblocks.5.attn.out_proj.bias FLOAT[768] 90bdd90d4b56
visual.transformer.resblocks.5.ln_1.bias FLOAT[768] 6364d03eb22e
visual.transformer.resblocks.5.ln_1.weight FLOAT[768] 19ae82bcf7e6
visual.transformer.resblocks.5.ln_2.bias FLOAT[768] 5c6356852ecb
visual.transformer.resblocks.5.ln_2.weight FLOAT[768] ae1ca8c060ab
visual.transformer.resblocks.5.mlp.c_fc.bias FLOAT[3072] ca9b54fd0522
visual.transformer.resblocks.5.mlp.c_proj.bias FLOAT[768] 2ae170703f56
visual.transformer.resblocks.6.attn.in_proj_bias FLOAT[2304] 33c448ccad77
visual.transformer.resblocks.6.attn.out_proj.bias FLOAT[768] 708ddf5cdbd1
visual.transformer.resblocks.6.ln_1.bias FLOAT[768] 0e1030b1b420
visual.transformer.resblocks.6.ln_1.weight FLOAT[768] 8094e4aa49e5
visual.transformer.resblocks.6.ln_2.bias FLOAT[768] 282ddf36d559
visual.transformer.resblocks.6.ln_2.weight FLOAT[768] 969e1345cf78
visual.transformer.resblocks.6.mlp.c_fc.bias FLOAT[3072] 4f547f0ca837
visual.transformer.resblocks.6.mlp.c_proj.bias FLOAT[768] 988a5fa2168a
visual.transformer.resblocks.7.attn.in_proj_bias FLOAT[2304] 9a4cba6b1d8d
visual.transformer.resblocks.7.attn.out_proj.bias FLOAT[768] bc7c930349bb
visual.transformer.resblocks.7.ln_1.bias FLOAT[768] 1b285e7fc0fa
visual.transformer.resblocks.7.ln_1.weight FLOAT[768] 54a84ff26735
visual.transformer.resblocks.7.ln_2.bias FLOAT[768] dc4fd964ed4a
visual.transformer.resblocks.7.ln_2.weight FLOAT[768] d1778ae9b165
visual.transformer.resblocks.7.mlp.c_fc.bias FLOAT[3072] 4b507e53fdd1
visual.transformer.resblocks.7.mlp.c_proj.bias FLOAT[768] acc9f25aa56c
visual.transformer.resblocks.8.attn.in_proj_bias FLOAT[2304] dd79652ddee4
visual.transformer.resblocks.8.attn.out_proj.bias FLOAT[768] 9c557d59f9e7
visual.transformer.resblocks.8.ln_1.bias FLOAT[768] 14dca1fc9452
visual.transformer.resblocks.8.ln_1.weight FLOAT[768] 3863c5fe487c
visual.transformer.resblocks.8.ln_2.bias FLOAT[768] bec8aaa02d0b
visual.transformer.resblocks.8.ln_2.weight FLOAT[768] c06a207a6c72
visual.transformer.resblocks.8.mlp.c_fc.bias FLOAT[3072] de42cbff2dd5
visual.transformer.resblocks.8.mlp.c_proj.bias FLOAT[768] 3316a2dded5c
visual.transformer.resblocks.9.attn.in_proj_bias FLOAT[2304] ffebf6eaed7a
visual.transformer.resblocks.9.attn.out_proj.bias FLOAT[768] a5491a315b46
visual.transformer.resblocks.9.ln_1.bias FLOAT[768] e3861fb2d7a9
visual.transformer.resblocks.9.ln_1.weight FLOAT[768] dfd2951f1353
visual.transformer.resblocks.9.ln_2.bias FLOAT[768] 3e6b81abf126
visual.transformer.resblocks.9.ln_2.weight FLOAT[768] de726a40eb67
visual.transformer.resblocks.9.mlp.c_fc.bias FLOAT[3072] 9ed0917fe47e
visual.transformer.resblocks.9.mlp.c_proj.bias FLOAT[768] c4cda4ff2d44
