<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (int32[batch,77] text) => (float[batch,512] text_embedding) 
   <
      float[batch,77,512] add_1071
      float[batch,77,512] add_1092
      float[batch,77,512] add_119
      float[batch,77,512] add_1207
      float[batch,77,512] add_1228
      float[batch,77,512] add_1343
      float[batch,77,512] add_1364
      float[batch,77,512] add_140
      float[batch,77,512] add_1479
      float[batch,77,512] add_1500
      float[batch,1,512] add_1500_pooled
      float[batch,1,512] add_1615
      float[batch,1,512] add_1636
      float[batch,77,512] add_255
      float[batch,77,512] add_276
      float[batch,77,512] add_391
      float[batch,77,512] add_4
      float[batch,77,512] add_412
      float[batch,77,512] add_527
      float[batch,77,512] add_548
      float[batch,77,512] add_663
      float[batch,77,512] add_684
      float[batch,77,512] add_799
      float[batch,77,512] add_820
      float[batch,77,512] add_935
      float[batch,77,512] add_956
      int64[batch] argmax
      float[batch,1] clamp_min
      float[batch,77,512] embedding
      float[batch,77,2048] gelu
      float[batch,77,2048] gelu_1
      float[batch,77,2048] gelu_10
      float[batch,1,2048] gelu_11
      float[batch,77,2048] gelu_2
      float[batch,77,2048] gelu_3
      float[batch,77,2048] gelu_4
      float[batch,77,2048] gelu_5
      float[batch,77,2048] gelu_6
      float[batch,77,2048] gelu_7
      float[batch,77,2048] gelu_8
      float[batch,77,2048] gelu_9
      float[batch,77,512] layer_norm
      float[batch,77,512] layer_norm_1
      float[batch,77,512] layer_norm_10
      float[batch,77,512] layer_norm_11
      float[batch,77,512] layer_norm_12
      float[batch,77,512] layer_norm_13
      float[batch,77,512] layer_norm_14
      float[batch,77,512] layer_norm_15
      float[batch,77,512] layer_norm_16
      float[batch,77,512] layer_norm_17
      float[batch,77,512] layer_norm_18
      float[batch,77,512] layer_norm_19
      float[batch,77,512] layer_norm_2
      float[batch,77,512] layer_norm_20
      float[batch,77,512] layer_norm_21
      float[batch,77,512] layer_norm_22
      float[batch,1,512] layer_norm_23
      float[batch,77,512] layer_norm_3
      float[batch,77,512] layer_norm_4
      float[batch,77,512] layer_norm_5
      float[batch,77,512] layer_norm_6
      float[batch,77,512] layer_norm_7
      float[batch,77,512] layer_norm_8
      float[batch,77,512] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,77,2048] linear_10
      float[batch,77,512] linear_11
      float[batch,77,2048] linear_14
      float[batch,77,512] linear_15
      float[batch,77,2048] linear_18
      float[batch,77,512] linear_19
      float[batch,77,2048] linear_2
      float[batch,77,2048] linear_22
      float[batch,77,512] linear_23
      float[batch,77,2048] linear_26
      float[batch,77,512] linear_27
      float[batch,77,512] linear_3
      float[batch,77,2048] linear_30
      float[batch,77,512] linear_31
      float[batch,77,2048] linear_34
      float[batch,77,512] linear_35
      float[batch,77,2048] linear_38
      float[batch,77,512] linear_39
      float[batch,77,2048] linear_42
      float[batch,77,512] linear_43
      float[batch,1,2048] linear_46
      float[batch,1,512] linear_47
      float[batch,77,2048] linear_6
      float[batch,77,512] linear_7
      float[batch,512] matmul
      float[batch,77,512] node_scaled_dot_product_attention_10_k
      float[batch,77,512] node_scaled_dot_product_attention_10_out
      float[batch,77,512] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_10_q
      float[batch,77,1536] node_scaled_dot_product_attention_10_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_10_v
      float[batch,77,512] node_scaled_dot_product_attention_11_k
      float[batch,1,512] node_scaled_dot_product_attention_11_out
      float[batch,1,512] node_scaled_dot_product_attention_11_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_11_q
      float[batch,77,1536] node_scaled_dot_product_attention_11_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_11_v
      float[batch,77,512] node_scaled_dot_product_attention_1_k
      float[batch,77,512] node_scaled_dot_product_attention_1_out
      float[batch,77,512] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_1_q
      float[batch,77,1536] node_scaled_dot_product_attention_1_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_1_v
      float[batch,77,512] node_scaled_dot_product_attention_2_k
      float[batch,77,512] node_scaled_dot_product_attention_2_out
      float[batch,77,512] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_2_q
      float[batch,77,1536] node_scaled_dot_product_attention_2_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_2_v
      float[batch,77,512] node_scaled_dot_product_attention_3_k
      float[batch,77,512] node_scaled_dot_product_attention_3_out
      float[batch,77,512] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_3_q
      float[batch,77,1536] node_scaled_dot_product_attention_3_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_3_v
      float[batch,77,512] node_scaled_dot_product_attention_4_k
      float[batch,77,512] node_scaled_dot_product_attention_4_out
      float[batch,77,512] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_4_q
      float[batch,77,1536] node_scaled_dot_product_attention_4_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_4_v
      float[batch,77,512] node_scaled_dot_product_attention_5_k
      float[batch,77,512] node_scaled_dot_product_attention_5_out
      float[batch,77,512] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_5_q
      float[batch,77,1536] node_scaled_dot_product_attention_5_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_5_v
      float[batch,77,512] node_scaled_dot_product_attention_6_k
      float[batch,77,512] node_scaled_dot_product_attention_6_out
      float[batch,77,512] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_6_q
      float[batch,77,1536] node_scaled_dot_product_attention_6_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_6_v
      float[batch,77,512] node_scaled_dot_product_attention_7_k
      float[batch,77,512] node_scaled_dot_product_attention_7_out
      float[batch,77,512] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_7_q
      float[batch,77,1536] node_scaled_dot_product_attention_7_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_7_v
      float[batch,77,512] node_scaled_dot_product_attention_8_k
      float[batch,77,512] node_scaled_dot_product_attention_8_out
      float[batch,77,512] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_8_q
      float[batch,77,1536] node_scaled_dot_product_attention_8_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_8_v
      float[batch,77,512] node_scaled_dot_product_attention_9_k
      float[batch,77,512] node_scaled_dot_product_attention_9_out
      float[batch,77,512] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_9_q
      float[batch,77,1536] node_scaled_dot_product_attention_9_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_9_v
      float[batch,77,512] node_scaled_dot_product_attention_k
      float[batch,77,512] node_scaled_dot_product_attention_out
      float[batch,77,512] node_scaled_dot_product_attention_out_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_q
      float[batch,77,1536] node_scaled_dot_product_attention_qkv
      float[batch,77,1536] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,77,512] node_scaled_dot_product_attention_v
      float[batch,77,512] scaled_dot_product_attention
      float[batch,77,512] scaled_dot_product_attention_1
      float[batch,77,512] scaled_dot_product_attention_10
      float[batch,77,512] scaled_dot_product_attention_11
      float[batch,1,512] scaled_dot_product_attention_11_pooled
      float[batch,77,512] scaled_dot_product_attention_2
      float[batch,77,512] scaled_dot_product_attention_3
      float[batch,77,512] scaled_dot_product_attention_4
      float[batch,77,512] scaled_dot_product_attention_5
      float[batch,77,512] scaled_dot_product_attention_6
      float[batch,77,512] scaled_dot_product_attention_7
      float[batch,77,512] scaled_dot_product_attention_8
      float[batch,77,512] scaled_dot_product_attention_9
      float[batch,77,2048] val_39
      float[batch,77,512] val_40
      float[batch,77,2048] val_41
      float[batch,77,512] val_42
      float[batch,77,2048] val_43
      float[batch,77,512] val_44
      float[batch,77,2048] val_45
      float[batch,77,512] val_46
      float[batch,77,2048] val_47
      float[batch,77,512] val_48
      float[batch,77,2048] val_49
      float[batch,77,512] val_50
      float[batch,77,2048] val_51
      float[batch,77,512] val_52
      float[batch,77,2048] val_53
      float[batch,77,512] val_54
      float[batch,77,2048] val_55
      float[batch,77,512] val_56
      float[batch,77,2048] val_57
      float[batch,77,512] val_58
      float[batch,77,2048] val_59
      float[batch,77,512] val_60
      float[batch,77] val_61
      float[batch,1,77] val_62
      float[batch,1,2048] val_63
      float[batch,1,512] val_64
      float[batch,1,512] val_65
      float[batch,512] val_66
   >
{
   val_38 = Gather <axis: int = 0> ("token_embedding.weight_fp16", text)
   embedding = Cast <to: int = 1> (val_38)
   add_4 = Add (embedding, positional_embedding)
   [node_layer_norm] layer_norm = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_4, "transformer.resblocks.0.ln_1.weight", "transformer.resblocks.0.ln_1.bias")
   [node_scaled_dot_product_attention_qkv_mm] node_scaled_dot_product_attention_qkv_mm_out = MatMul (layer_norm, val_2)
   [node_scaled_dot_product_attention_qkv_bias] node_scaled_dot_product_attention_qkv = Add (node_scaled_dot_product_attention_qkv_mm_out, "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_3x512)
   scaled_dot_product_attention = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.0.attn.out_proj.bias")
   add_119 = Add (add_4, node_scaled_dot_product_attention_out)
   layer_norm_1 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_119, "transformer.resblocks.0.ln_2.weight", "transformer.resblocks.0.ln_2.bias")
   val_39 = MatMul (layer_norm_1, val_3)
   linear_2 = Add (val_39, "transformer.resblocks.0.mlp.c_fc.bias")
   [node_gelu] gelu = Gelu <approximate: string = "none"> (linear_2)
   val_40 = MatMul (gelu, val_4)
   linear_3 = Add (val_40, "transformer.resblocks.0.mlp.c_proj.bias")
   add_140 = Add (add_119, linear_3)
   layer_norm_2 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_140, "transformer.resblocks.1.ln_1.weight", "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_2, val_5)
   [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, "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_3x512)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.1.attn.out_proj.bias")
   add_255 = Add (add_140, node_scaled_dot_product_attention_1_out)
   layer_norm_3 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_255, "transformer.resblocks.1.ln_2.weight", "transformer.resblocks.1.ln_2.bias")
   val_41 = MatMul (layer_norm_3, val_6)
   linear_6 = Add (val_41, "transformer.resblocks.1.mlp.c_fc.bias")
   gelu_1 = Gelu <approximate: string = "none"> (linear_6)
   val_42 = MatMul (gelu_1, val_7)
   linear_7 = Add (val_42, "transformer.resblocks.1.mlp.c_proj.bias")
   add_276 = Add (add_255, linear_7)
   layer_norm_4 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_276, "transformer.resblocks.2.ln_1.weight", "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_4, val_8)
   [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, "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_3x512)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.2.attn.out_proj.bias")
   add_391 = Add (add_276, node_scaled_dot_product_attention_2_out)
   layer_norm_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_391, "transformer.resblocks.2.ln_2.weight", "transformer.resblocks.2.ln_2.bias")
   val_43 = MatMul (layer_norm_5, val_9)
   linear_10 = Add (val_43, "transformer.resblocks.2.mlp.c_fc.bias")
   gelu_2 = Gelu <approximate: string = "none"> (linear_10)
   val_44 = MatMul (gelu_2, val_10)
   linear_11 = Add (val_44, "transformer.resblocks.2.mlp.c_proj.bias")
   add_412 = Add (add_391, linear_11)
   layer_norm_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_412, "transformer.resblocks.3.ln_1.weight", "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_6, val_11)
   [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, "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_3x512)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.3.attn.out_proj.bias")
   add_527 = Add (add_412, node_scaled_dot_product_attention_3_out)
   layer_norm_7 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_527, "transformer.resblocks.3.ln_2.weight", "transformer.resblocks.3.ln_2.bias")
   val_45 = MatMul (layer_norm_7, val_12)
   linear_14 = Add (val_45, "transformer.resblocks.3.mlp.c_fc.bias")
   gelu_3 = Gelu <approximate: string = "none"> (linear_14)
   val_46 = MatMul (gelu_3, val_13)
   linear_15 = Add (val_46, "transformer.resblocks.3.mlp.c_proj.bias")
   add_548 = Add (add_527, linear_15)
   layer_norm_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_548, "transformer.resblocks.4.ln_1.weight", "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_8, val_14)
   [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, "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_3x512)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.4.attn.out_proj.bias")
   add_663 = Add (add_548, node_scaled_dot_product_attention_4_out)
   layer_norm_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_663, "transformer.resblocks.4.ln_2.weight", "transformer.resblocks.4.ln_2.bias")
   val_47 = MatMul (layer_norm_9, val_15)
   linear_18 = Add (val_47, "transformer.resblocks.4.mlp.c_fc.bias")
   gelu_4 = Gelu <approximate: string = "none"> (linear_18)
   val_48 = MatMul (gelu_4, val_16)
   linear_19 = Add (val_48, "transformer.resblocks.4.mlp.c_proj.bias")
   add_684 = Add (add_663, linear_19)
   layer_norm_10 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_684, "transformer.resblocks.5.ln_1.weight", "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_10, val_17)
   [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, "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_3x512)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.5.attn.out_proj.bias")
   add_799 = Add (add_684, node_scaled_dot_product_attention_5_out)
   layer_norm_11 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_799, "transformer.resblocks.5.ln_2.weight", "transformer.resblocks.5.ln_2.bias")
   val_49 = MatMul (layer_norm_11, val_18)
   linear_22 = Add (val_49, "transformer.resblocks.5.mlp.c_fc.bias")
   gelu_5 = Gelu <approximate: string = "none"> (linear_22)
   val_50 = MatMul (gelu_5, val_19)
   linear_23 = Add (val_50, "transformer.resblocks.5.mlp.c_proj.bias")
   add_820 = Add (add_799, linear_23)
   layer_norm_12 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_820, "transformer.resblocks.6.ln_1.weight", "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_12, val_20)
   [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, "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_3x512)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.6.attn.out_proj.bias")
   add_935 = Add (add_820, node_scaled_dot_product_attention_6_out)
   layer_norm_13 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_935, "transformer.resblocks.6.ln_2.weight", "transformer.resblocks.6.ln_2.bias")
   val_51 = MatMul (layer_norm_13, val_21)
   linear_26 = Add (val_51, "transformer.resblocks.6.mlp.c_fc.bias")
   gelu_6 = Gelu <approximate: string = "none"> (linear_26)
   val_52 = MatMul (gelu_6, val_22)
   linear_27 = Add (val_52, "transformer.resblocks.6.mlp.c_proj.bias")
   add_956 = Add (add_935, linear_27)
   layer_norm_14 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_956, "transformer.resblocks.7.ln_1.weight", "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_14, val_23)
   [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, "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_3x512)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.7.attn.out_proj.bias")
   add_1071 = Add (add_956, node_scaled_dot_product_attention_7_out)
   layer_norm_15 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1071, "transformer.resblocks.7.ln_2.weight", "transformer.resblocks.7.ln_2.bias")
   val_53 = MatMul (layer_norm_15, val_24)
   linear_30 = Add (val_53, "transformer.resblocks.7.mlp.c_fc.bias")
   gelu_7 = Gelu <approximate: string = "none"> (linear_30)
   val_54 = MatMul (gelu_7, val_25)
   linear_31 = Add (val_54, "transformer.resblocks.7.mlp.c_proj.bias")
   add_1092 = Add (add_1071, linear_31)
   layer_norm_16 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1092, "transformer.resblocks.8.ln_1.weight", "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_16, val_26)
   [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, "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_3x512)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.8.attn.out_proj.bias")
   add_1207 = Add (add_1092, node_scaled_dot_product_attention_8_out)
   layer_norm_17 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1207, "transformer.resblocks.8.ln_2.weight", "transformer.resblocks.8.ln_2.bias")
   val_55 = MatMul (layer_norm_17, val_27)
   linear_34 = Add (val_55, "transformer.resblocks.8.mlp.c_fc.bias")
   gelu_8 = Gelu <approximate: string = "none"> (linear_34)
   val_56 = MatMul (gelu_8, val_28)
   linear_35 = Add (val_56, "transformer.resblocks.8.mlp.c_proj.bias")
   add_1228 = Add (add_1207, linear_35)
   layer_norm_18 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1228, "transformer.resblocks.9.ln_1.weight", "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_18, val_29)
   [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, "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_3x512)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.9.attn.out_proj.bias")
   add_1343 = Add (add_1228, node_scaled_dot_product_attention_9_out)
   layer_norm_19 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1343, "transformer.resblocks.9.ln_2.weight", "transformer.resblocks.9.ln_2.bias")
   val_57 = MatMul (layer_norm_19, val_30)
   linear_38 = Add (val_57, "transformer.resblocks.9.mlp.c_fc.bias")
   gelu_9 = Gelu <approximate: string = "none"> (linear_38)
   val_58 = MatMul (gelu_9, val_31)
   linear_39 = Add (val_58, "transformer.resblocks.9.mlp.c_proj.bias")
   add_1364 = Add (add_1343, linear_39)
   layer_norm_20 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1364, "transformer.resblocks.10.ln_1.weight", "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_20, val_32)
   [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, "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_3x512)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, 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, "transformer.resblocks.10.attn.out_proj.bias")
   add_1479 = Add (add_1364, node_scaled_dot_product_attention_10_out)
   layer_norm_21 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1479, "transformer.resblocks.10.ln_2.weight", "transformer.resblocks.10.ln_2.bias")
   val_59 = MatMul (layer_norm_21, val_33)
   linear_42 = Add (val_59, "transformer.resblocks.10.mlp.c_fc.bias")
   gelu_10 = Gelu <approximate: string = "none"> (linear_42)
   val_60 = MatMul (gelu_10, val_34)
   linear_43 = Add (val_60, "transformer.resblocks.10.mlp.c_proj.bias")
   add_1500 = Add (add_1479, linear_43)
   layer_norm_22 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1500, "transformer.resblocks.11.ln_1.weight", "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_22, val_35)
   [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, "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_3x512)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 1, kv_num_heads: int = 8, q_num_heads: int = 8, qk_matmul_output_mode: int = 0, softcap: float = 0> (node_scaled_dot_product_attention_11_q, node_scaled_dot_product_attention_11_k, node_scaled_dot_product_attention_11_v)
   [node_argmax] argmax = ArgMax <axis: int = -1, keepdims: int = 0, select_last_index: int = 0> (text)
   val_61 = Gather <axis: int = 0> (val_755_eye, argmax)
   val_62 = Unsqueeze (val_61, val_755_axes1)
   [pool_hoist_scaled_dot_product_attention_11] scaled_dot_product_attention_11_pooled = MatMul (val_62, scaled_dot_product_attention_11)
   [node_scaled_dot_product_attention_11_out_mm] node_scaled_dot_product_attention_11_out_mm_out = MatMul (scaled_dot_product_attention_11_pooled, 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, "transformer.resblocks.11.attn.out_proj.bias")
   [pool_hoist_add_1500] add_1500_pooled = MatMul (val_62, add_1500)
   add_1615 = Add (add_1500_pooled, node_scaled_dot_product_attention_11_out)
   layer_norm_23 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1615, "transformer.resblocks.11.ln_2.weight", "transformer.resblocks.11.ln_2.bias")
   val_63 = MatMul (layer_norm_23, val_36)
   linear_46 = Add (val_63, "transformer.resblocks.11.mlp.c_fc.bias")
   gelu_11 = Gelu <approximate: string = "none"> (linear_46)
   val_64 = MatMul (gelu_11, val_37)
   linear_47 = Add (val_64, "transformer.resblocks.11.mlp.c_proj.bias")
   add_1636 = Add (add_1615, linear_47)
   val_65 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (add_1636, "ln_final.weight", "ln_final.bias")
   val_66 = Squeeze (val_65, val_755_axes1)
   [node_matmul] matmul = MatMul (val_66, text_projection)
   [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] text_embedding = Div (matmul, clamp_min)
}

weights:
attn3d_split_3x512 INT64[3] b4aa8df238da
ln_final.bias FLOAT[512] 57b7b7880e3e
ln_final.weight FLOAT[512] 6be5899856c8
node_scaled_dot_product_attention_10_wo_t FLOAT[512,512] bb295319b806
node_scaled_dot_product_attention_11_wo_t FLOAT[512,512] 77cb69e9cca0
node_scaled_dot_product_attention_1_wo_t FLOAT[512,512] 600ac2cdd837
node_scaled_dot_product_attention_2_wo_t FLOAT[512,512] 0ebfafc50906
node_scaled_dot_product_attention_3_wo_t FLOAT[512,512] 7a4f37791535
node_scaled_dot_product_attention_4_wo_t FLOAT[512,512] 87034196b49a
node_scaled_dot_product_attention_5_wo_t FLOAT[512,512] 1336235b89f2
node_scaled_dot_product_attention_6_wo_t FLOAT[512,512] ddc77000f87d
node_scaled_dot_product_attention_7_wo_t FLOAT[512,512] 361be7e0bbd8
node_scaled_dot_product_attention_8_wo_t FLOAT[512,512] 85a233ff6b1f
node_scaled_dot_product_attention_9_wo_t FLOAT[512,512] 5fba72dbe8e9
node_scaled_dot_product_attention_wo_t FLOAT[512,512] 9b0f92cb0847
positional_embedding FLOAT[77,512] 4fc6e1b8a86c
text_projection FLOAT[512,512] 0a0c1688d0a3
token_embedding.weight_fp16 FLOAT16[49408,512] 6f7a56cf4748
transformer.resblocks.0.attn.in_proj_bias FLOAT[1536] 8a3fd1b6e4de
transformer.resblocks.0.attn.out_proj.bias FLOAT[512] c4b2eab5e286
transformer.resblocks.0.ln_1.bias FLOAT[512] af30401c4f3a
transformer.resblocks.0.ln_1.weight FLOAT[512] d5450b8b2a96
transformer.resblocks.0.ln_2.bias FLOAT[512] ba730b2fdd81
transformer.resblocks.0.ln_2.weight FLOAT[512] 273da9666bbe
transformer.resblocks.0.mlp.c_fc.bias FLOAT[2048] 235e0da514e4
transformer.resblocks.0.mlp.c_proj.bias FLOAT[512] 37c9ed0b3b7e
transformer.resblocks.1.attn.in_proj_bias FLOAT[1536] 7d6dcfb9424f
transformer.resblocks.1.attn.out_proj.bias FLOAT[512] 63c19e5e3e28
transformer.resblocks.1.ln_1.bias FLOAT[512] 2ef6aa8a347e
transformer.resblocks.1.ln_1.weight FLOAT[512] b177edd06816
transformer.resblocks.1.ln_2.bias FLOAT[512] f7110a1db2a0
transformer.resblocks.1.ln_2.weight FLOAT[512] 4ad79c155cf2
transformer.resblocks.1.mlp.c_fc.bias FLOAT[2048] cba9083463c9
transformer.resblocks.1.mlp.c_proj.bias FLOAT[512] 2966a89addd0
transformer.resblocks.10.attn.in_proj_bias FLOAT[1536] 9d3d9094ef17
transformer.resblocks.10.attn.out_proj.bias FLOAT[512] 81b72f8cb3c1
transformer.resblocks.10.ln_1.bias FLOAT[512] 3b0a13c54fe9
transformer.resblocks.10.ln_1.weight FLOAT[512] 5b2e35097565
transformer.resblocks.10.ln_2.bias FLOAT[512] f924ea302a8a
transformer.resblocks.10.ln_2.weight FLOAT[512] 4bb3e4682db3
transformer.resblocks.10.mlp.c_fc.bias FLOAT[2048] 6188cc24160b
transformer.resblocks.10.mlp.c_proj.bias FLOAT[512] e0e7f4c4b121
transformer.resblocks.11.attn.in_proj_bias FLOAT[1536] be74157d03a0
transformer.resblocks.11.attn.out_proj.bias FLOAT[512] f79768cbc5bb
transformer.resblocks.11.ln_1.bias FLOAT[512] ab77e11d9285
transformer.resblocks.11.ln_1.weight FLOAT[512] 4b8d7c7418e2
transformer.resblocks.11.ln_2.bias FLOAT[512] 9ababa02d7cb
transformer.resblocks.11.ln_2.weight FLOAT[512] 172bb6ca50e6
transformer.resblocks.11.mlp.c_fc.bias FLOAT[2048] 5d3b122729b5
transformer.resblocks.11.mlp.c_proj.bias FLOAT[512] 2cc6a713a015
transformer.resblocks.2.attn.in_proj_bias FLOAT[1536] 2df3e307db1b
transformer.resblocks.2.attn.out_proj.bias FLOAT[512] 8db0079cde99
transformer.resblocks.2.ln_1.bias FLOAT[512] 7c1e6f063e4f
transformer.resblocks.2.ln_1.weight FLOAT[512] dfe82b4534a5
transformer.resblocks.2.ln_2.bias FLOAT[512] c406071eaf79
transformer.resblocks.2.ln_2.weight FLOAT[512] 33b4d070d91c
transformer.resblocks.2.mlp.c_fc.bias FLOAT[2048] c03baf7f1e0f
transformer.resblocks.2.mlp.c_proj.bias FLOAT[512] de807400a9e8
transformer.resblocks.3.attn.in_proj_bias FLOAT[1536] 5cec951191fd
transformer.resblocks.3.attn.out_proj.bias FLOAT[512] 2ca1e0f0aa39
transformer.resblocks.3.ln_1.bias FLOAT[512] 81e18bf2dbbf
transformer.resblocks.3.ln_1.weight FLOAT[512] e9ecec06a679
transformer.resblocks.3.ln_2.bias FLOAT[512] ae2e5e3c6d23
transformer.resblocks.3.ln_2.weight FLOAT[512] 679f1a56427f
transformer.resblocks.3.mlp.c_fc.bias FLOAT[2048] cbc6fafe31cf
transformer.resblocks.3.mlp.c_proj.bias FLOAT[512] 85880d49b9a4
transformer.resblocks.4.attn.in_proj_bias FLOAT[1536] 33b4ac929f71
transformer.resblocks.4.attn.out_proj.bias FLOAT[512] dfd97c83afc4
transformer.resblocks.4.ln_1.bias FLOAT[512] 46854389c16f
transformer.resblocks.4.ln_1.weight FLOAT[512] effd64147033
transformer.resblocks.4.ln_2.bias FLOAT[512] d4c0e87afca3
transformer.resblocks.4.ln_2.weight FLOAT[512] 4456e45d3153
transformer.resblocks.4.mlp.c_fc.bias FLOAT[2048] 9b12224de8c0
transformer.resblocks.4.mlp.c_proj.bias FLOAT[512] 6eed4a25897b
transformer.resblocks.5.attn.in_proj_bias FLOAT[1536] 4aba65e6fdf7
transformer.resblocks.5.attn.out_proj.bias FLOAT[512] aac5f453d1f2
transformer.resblocks.5.ln_1.bias FLOAT[512] 8af57a7449a7
transformer.resblocks.5.ln_1.weight FLOAT[512] 32e1ebe57b16
transformer.resblocks.5.ln_2.bias FLOAT[512] a3123da682aa
transformer.resblocks.5.ln_2.weight FLOAT[512] 4c119e49a46f
transformer.resblocks.5.mlp.c_fc.bias FLOAT[2048] 60e82febd943
transformer.resblocks.5.mlp.c_proj.bias FLOAT[512] c8f845cb2937
transformer.resblocks.6.attn.in_proj_bias FLOAT[1536] 7958a8701a3a
transformer.resblocks.6.attn.out_proj.bias FLOAT[512] beb2b8f8306d
transformer.resblocks.6.ln_1.bias FLOAT[512] 07166c751f07
transformer.resblocks.6.ln_1.weight FLOAT[512] 5bd22b5d7311
transformer.resblocks.6.ln_2.bias FLOAT[512] ad62c8c17352
transformer.resblocks.6.ln_2.weight FLOAT[512] 09e4616c9640
transformer.resblocks.6.mlp.c_fc.bias FLOAT[2048] 85988f6aa307
transformer.resblocks.6.mlp.c_proj.bias FLOAT[512] 07d9753905c2
transformer.resblocks.7.attn.in_proj_bias FLOAT[1536] b76aa0619aac
transformer.resblocks.7.attn.out_proj.bias FLOAT[512] 385b6cf2192f
transformer.resblocks.7.ln_1.bias FLOAT[512] 0774c88f8c96
transformer.resblocks.7.ln_1.weight FLOAT[512] f513e5b2f0a4
transformer.resblocks.7.ln_2.bias FLOAT[512] 65efe8aa07f4
transformer.resblocks.7.ln_2.weight FLOAT[512] e14f04bab200
transformer.resblocks.7.mlp.c_fc.bias FLOAT[2048] 4f506fe6dda4
transformer.resblocks.7.mlp.c_proj.bias FLOAT[512] 620e36ae93ab
transformer.resblocks.8.attn.in_proj_bias FLOAT[1536] f51723a7ca75
transformer.resblocks.8.attn.out_proj.bias FLOAT[512] ca4eee76c343
transformer.resblocks.8.ln_1.bias FLOAT[512] c1d492dccfb8
transformer.resblocks.8.ln_1.weight FLOAT[512] fcd637a5063f
transformer.resblocks.8.ln_2.bias FLOAT[512] 8a097a0f8d96
transformer.resblocks.8.ln_2.weight FLOAT[512] cf06b593d1fd
transformer.resblocks.8.mlp.c_fc.bias FLOAT[2048] 952b6a7da1d6
transformer.resblocks.8.mlp.c_proj.bias FLOAT[512] 47dbf88ef6b0
transformer.resblocks.9.attn.in_proj_bias FLOAT[1536] 0f6b85e610ae
transformer.resblocks.9.attn.out_proj.bias FLOAT[512] 5c7ca5675481
transformer.resblocks.9.ln_1.bias FLOAT[512] 0fd4752bab77
transformer.resblocks.9.ln_1.weight FLOAT[512] 61333e15e088
transformer.resblocks.9.ln_2.bias FLOAT[512] 36d161d8954a
transformer.resblocks.9.ln_2.weight FLOAT[512] b4648bde250a
transformer.resblocks.9.mlp.c_fc.bias FLOAT[2048] e971ff0cc7a5
transformer.resblocks.9.mlp.c_proj.bias FLOAT[512] c9481e919fe8
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[2048,512] a020d9f80d42
val_11 FLOAT[512,1536] aff5491b6177
val_12 FLOAT[512,2048] f7f0ade087f9
val_13 FLOAT[2048,512] 60f127f8995e
val_14 FLOAT[512,1536] 0ab00941825d
val_15 FLOAT[512,2048] c07cefa32652
val_16 FLOAT[2048,512] 0c18d0a557d9
val_17 FLOAT[512,1536] 905996733d10
val_18 FLOAT[512,2048] 58f4bc2d059a
val_19 FLOAT[2048,512] 1c6b5349fe59
val_2 FLOAT[512,1536] 5057132df526
val_20 FLOAT[512,1536] c855c443eee3
val_21 FLOAT[512,2048] a013ba1deb3f
val_22 FLOAT[2048,512] d58f3218d49b
val_23 FLOAT[512,1536] 9151d966023a
val_24 FLOAT[512,2048] 874ab947acf8
val_25 FLOAT[2048,512] d1034e4bec66
val_26 FLOAT[512,1536] 526b8c88cab7
val_27 FLOAT[512,2048] cdc7d5195eca
val_28 FLOAT[2048,512] f53e5bb86f8e
val_29 FLOAT[512,1536] 9c8ca4998c04
val_3 FLOAT[512,2048] 677b664b6415
val_30 FLOAT[512,2048] 23e64009fdc8
val_31 FLOAT[2048,512] 13d832fa59e1
val_32 FLOAT[512,1536] 41500a170e15
val_33 FLOAT[512,2048] 541bd8307a6c
val_34 FLOAT[2048,512] 0e0788078ae6
val_35 FLOAT[512,1536] 1ef23f2cd973
val_36 FLOAT[512,2048] b00570195ab0
val_37 FLOAT[2048,512] 4ab3fa06af91
val_4 FLOAT[2048,512] 27e056cd45b7
val_5 FLOAT[512,1536] 660ed51cf707
val_6 FLOAT[512,2048] d9b851d1318b
val_7 FLOAT[2048,512] ffd994dfb3d9
val_755_axes1 INT64[1] 7c9fa136d441
val_755_eye FLOAT[77,77] 39ccea14e08c
val_8 FLOAT[512,1536] 6dee5e196a92
val_9 FLOAT[512,2048] 54a547210b53
