<
   ir_version: 10,
   opset_import: ["" : 23],
   producer_name: "pytorch"
>
main_graph (int32[batch,77] text) => (float[batch,768] text_embedding) 
   <
      float[batch,77,768] add_1071
      float[batch,77,768] add_1092
      float[batch,77,768] add_119
      float[batch,77,768] add_1207
      float[batch,77,768] add_1228
      float[batch,77,768] add_1343
      float[batch,77,768] add_1364
      float[batch,77,768] add_140
      float[batch,77,768] add_1479
      float[batch,77,768] add_1500
      float[batch,1,768] add_1500_pooled
      float[batch,1,768] add_1615
      float[batch,1,768] add_1636
      float[batch,77,768] add_255
      float[batch,77,768] add_276
      float[batch,77,768] add_391
      float[batch,77,768] add_4
      float[batch,77,768] add_412
      float[batch,77,768] add_527
      float[batch,77,768] add_548
      float[batch,77,768] add_663
      float[batch,77,768] add_684
      float[batch,77,768] add_799
      float[batch,77,768] add_820
      float[batch,77,768] add_935
      float[batch,77,768] add_956
      int64[batch] argmax
      float[batch,1] clamp_min
      float[batch,77,768] embedding
      float[batch,77,3072] gelu
      float[batch,77,3072] gelu_1
      float[batch,77,3072] gelu_10
      float[batch,1,3072] gelu_11
      float[batch,77,3072] gelu_2
      float[batch,77,3072] gelu_3
      float[batch,77,3072] gelu_4
      float[batch,77,3072] gelu_5
      float[batch,77,3072] gelu_6
      float[batch,77,3072] gelu_7
      float[batch,77,3072] gelu_8
      float[batch,77,3072] gelu_9
      float[batch,77,768] layer_norm
      float[batch,77,768] layer_norm_1
      float[batch,77,768] layer_norm_10
      float[batch,77,768] layer_norm_11
      float[batch,77,768] layer_norm_12
      float[batch,77,768] layer_norm_13
      float[batch,77,768] layer_norm_14
      float[batch,77,768] layer_norm_15
      float[batch,77,768] layer_norm_16
      float[batch,77,768] layer_norm_17
      float[batch,77,768] layer_norm_18
      float[batch,77,768] layer_norm_19
      float[batch,77,768] layer_norm_2
      float[batch,77,768] layer_norm_20
      float[batch,77,768] layer_norm_21
      float[batch,77,768] layer_norm_22
      float[batch,1,768] layer_norm_23
      float[batch,77,768] layer_norm_3
      float[batch,77,768] layer_norm_4
      float[batch,77,768] layer_norm_5
      float[batch,77,768] layer_norm_6
      float[batch,77,768] layer_norm_7
      float[batch,77,768] layer_norm_8
      float[batch,77,768] layer_norm_9
      float[batch,1] linalg_vector_norm
      float[batch,77,3072] linear_10
      float[batch,77,768] linear_11
      float[batch,77,3072] linear_14
      float[batch,77,768] linear_15
      float[batch,77,3072] linear_18
      float[batch,77,768] linear_19
      float[batch,77,3072] linear_2
      float[batch,77,3072] linear_22
      float[batch,77,768] linear_23
      float[batch,77,3072] linear_26
      float[batch,77,768] linear_27
      float[batch,77,768] linear_3
      float[batch,77,3072] linear_30
      float[batch,77,768] linear_31
      float[batch,77,3072] linear_34
      float[batch,77,768] linear_35
      float[batch,77,3072] linear_38
      float[batch,77,768] linear_39
      float[batch,77,3072] linear_42
      float[batch,77,768] linear_43
      float[batch,1,3072] linear_46
      float[batch,1,768] linear_47
      float[batch,77,3072] linear_6
      float[batch,77,768] linear_7
      float[batch,768] matmul
      float[batch,77,768] node_scaled_dot_product_attention_10_k
      float[batch,77,768] node_scaled_dot_product_attention_10_out
      float[batch,77,768] node_scaled_dot_product_attention_10_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_10_q
      float[batch,77,2304] node_scaled_dot_product_attention_10_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_10_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_10_v
      float[batch,77,768] 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,77,768] node_scaled_dot_product_attention_11_q
      float[batch,77,2304] node_scaled_dot_product_attention_11_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_11_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_11_v
      float[batch,77,768] node_scaled_dot_product_attention_1_k
      float[batch,77,768] node_scaled_dot_product_attention_1_out
      float[batch,77,768] node_scaled_dot_product_attention_1_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_1_q
      float[batch,77,2304] node_scaled_dot_product_attention_1_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_1_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_1_v
      float[batch,77,768] node_scaled_dot_product_attention_2_k
      float[batch,77,768] node_scaled_dot_product_attention_2_out
      float[batch,77,768] node_scaled_dot_product_attention_2_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_2_q
      float[batch,77,2304] node_scaled_dot_product_attention_2_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_2_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_2_v
      float[batch,77,768] node_scaled_dot_product_attention_3_k
      float[batch,77,768] node_scaled_dot_product_attention_3_out
      float[batch,77,768] node_scaled_dot_product_attention_3_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_3_q
      float[batch,77,2304] node_scaled_dot_product_attention_3_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_3_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_3_v
      float[batch,77,768] node_scaled_dot_product_attention_4_k
      float[batch,77,768] node_scaled_dot_product_attention_4_out
      float[batch,77,768] node_scaled_dot_product_attention_4_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_4_q
      float[batch,77,2304] node_scaled_dot_product_attention_4_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_4_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_4_v
      float[batch,77,768] node_scaled_dot_product_attention_5_k
      float[batch,77,768] node_scaled_dot_product_attention_5_out
      float[batch,77,768] node_scaled_dot_product_attention_5_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_5_q
      float[batch,77,2304] node_scaled_dot_product_attention_5_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_5_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_5_v
      float[batch,77,768] node_scaled_dot_product_attention_6_k
      float[batch,77,768] node_scaled_dot_product_attention_6_out
      float[batch,77,768] node_scaled_dot_product_attention_6_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_6_q
      float[batch,77,2304] node_scaled_dot_product_attention_6_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_6_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_6_v
      float[batch,77,768] node_scaled_dot_product_attention_7_k
      float[batch,77,768] node_scaled_dot_product_attention_7_out
      float[batch,77,768] node_scaled_dot_product_attention_7_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_7_q
      float[batch,77,2304] node_scaled_dot_product_attention_7_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_7_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_7_v
      float[batch,77,768] node_scaled_dot_product_attention_8_k
      float[batch,77,768] node_scaled_dot_product_attention_8_out
      float[batch,77,768] node_scaled_dot_product_attention_8_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_8_q
      float[batch,77,2304] node_scaled_dot_product_attention_8_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_8_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_8_v
      float[batch,77,768] node_scaled_dot_product_attention_9_k
      float[batch,77,768] node_scaled_dot_product_attention_9_out
      float[batch,77,768] node_scaled_dot_product_attention_9_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_9_q
      float[batch,77,2304] node_scaled_dot_product_attention_9_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_9_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_9_v
      float[batch,77,768] node_scaled_dot_product_attention_k
      float[batch,77,768] node_scaled_dot_product_attention_out
      float[batch,77,768] node_scaled_dot_product_attention_out_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_q
      float[batch,77,2304] node_scaled_dot_product_attention_qkv
      float[batch,77,2304] node_scaled_dot_product_attention_qkv_mm_out
      float[batch,77,768] node_scaled_dot_product_attention_v
      float[batch,77,768] scaled_dot_product_attention
      float[batch,77,768] scaled_dot_product_attention_1
      float[batch,77,768] scaled_dot_product_attention_10
      float[batch,77,768] scaled_dot_product_attention_11
      float[batch,1,768] scaled_dot_product_attention_11_pooled
      float[batch,77,768] scaled_dot_product_attention_2
      float[batch,77,768] scaled_dot_product_attention_3
      float[batch,77,768] scaled_dot_product_attention_4
      float[batch,77,768] scaled_dot_product_attention_5
      float[batch,77,768] scaled_dot_product_attention_6
      float[batch,77,768] scaled_dot_product_attention_7
      float[batch,77,768] scaled_dot_product_attention_8
      float[batch,77,768] scaled_dot_product_attention_9
      float[batch,77,3072] val_39
      float[batch,77,768] val_40
      float[batch,77,3072] val_41
      float[batch,77,768] val_42
      float[batch,77,3072] val_43
      float[batch,77,768] val_44
      float[batch,77,3072] val_45
      float[batch,77,768] val_46
      float[batch,77,3072] val_47
      float[batch,77,768] val_48
      float[batch,77,3072] val_49
      float[batch,77,768] val_50
      float[batch,77,3072] val_51
      float[batch,77,768] val_52
      float[batch,77,3072] val_53
      float[batch,77,768] val_54
      float[batch,77,3072] val_55
      float[batch,77,768] val_56
      float[batch,77,3072] val_57
      float[batch,77,768] val_58
      float[batch,77,3072] val_59
      float[batch,77,768] val_60
      float[batch,77] val_61
      float[batch,1,77] val_62
      float[batch,1,3072] val_63
      float[batch,1,768] val_64
      float[batch,1,768] val_65
      float[batch,768] 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_3x768)
   scaled_dot_product_attention = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_1 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_2 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_3 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_4 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_5 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_6 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_7 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_8 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_9 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_10 = Attention <is_causal: int = 1, 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, "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_3x768)
   scaled_dot_product_attention_11 = Attention <is_causal: int = 1, 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, 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_3x768 INT64[3] eca50b2daf34
ln_final.bias FLOAT[768] 30c84addd7dc
ln_final.weight FLOAT[768] 936045b63db3
node_scaled_dot_product_attention_10_wo_t FLOAT[768,768] f5fd8960b58d
node_scaled_dot_product_attention_11_wo_t FLOAT[768,768] d2850462e0b9
node_scaled_dot_product_attention_1_wo_t FLOAT[768,768] cf9abbd271d1
node_scaled_dot_product_attention_2_wo_t FLOAT[768,768] 5ded100b6e84
node_scaled_dot_product_attention_3_wo_t FLOAT[768,768] 01024b85a06e
node_scaled_dot_product_attention_4_wo_t FLOAT[768,768] 0b9acec4b2e6
node_scaled_dot_product_attention_5_wo_t FLOAT[768,768] cbca05b9d9fe
node_scaled_dot_product_attention_6_wo_t FLOAT[768,768] 5e9ab0f91a45
node_scaled_dot_product_attention_7_wo_t FLOAT[768,768] 255febaa4d9c
node_scaled_dot_product_attention_8_wo_t FLOAT[768,768] df7ff8890f7c
node_scaled_dot_product_attention_9_wo_t FLOAT[768,768] 099b6af4bf51
node_scaled_dot_product_attention_wo_t FLOAT[768,768] 7c7e21a8f1d8
positional_embedding FLOAT[77,768] 74b83c21f924
text_projection FLOAT[768,768] a7db3e5b8230
token_embedding.weight_fp16 FLOAT16[49408,768] 3d31c5df8866
transformer.resblocks.0.attn.in_proj_bias FLOAT[2304] b9f289f968f2
transformer.resblocks.0.attn.out_proj.bias FLOAT[768] eec5507eadfd
transformer.resblocks.0.ln_1.bias FLOAT[768] b45fd3874a2a
transformer.resblocks.0.ln_1.weight FLOAT[768] 22c2648b8f74
transformer.resblocks.0.ln_2.bias FLOAT[768] 7e8b79893c2b
transformer.resblocks.0.ln_2.weight FLOAT[768] 495854802ac1
transformer.resblocks.0.mlp.c_fc.bias FLOAT[3072] 7fb7cc86028b
transformer.resblocks.0.mlp.c_proj.bias FLOAT[768] 47072acf49d3
transformer.resblocks.1.attn.in_proj_bias FLOAT[2304] c4292cbf9071
transformer.resblocks.1.attn.out_proj.bias FLOAT[768] 66cbfe7fbf07
transformer.resblocks.1.ln_1.bias FLOAT[768] f4f4a2723d71
transformer.resblocks.1.ln_1.weight FLOAT[768] 8424b7d6f5bf
transformer.resblocks.1.ln_2.bias FLOAT[768] aaff5c96703b
transformer.resblocks.1.ln_2.weight FLOAT[768] 4e76bad46dc6
transformer.resblocks.1.mlp.c_fc.bias FLOAT[3072] 523c3986c9dc
transformer.resblocks.1.mlp.c_proj.bias FLOAT[768] 49445537ffc6
transformer.resblocks.10.attn.in_proj_bias FLOAT[2304] e40baa6a8bf4
transformer.resblocks.10.attn.out_proj.bias FLOAT[768] 63894b912d6b
transformer.resblocks.10.ln_1.bias FLOAT[768] 41630195b37b
transformer.resblocks.10.ln_1.weight FLOAT[768] 91b4e4ec3f1d
transformer.resblocks.10.ln_2.bias FLOAT[768] e0d4ab114055
transformer.resblocks.10.ln_2.weight FLOAT[768] 7a05f33ac5cb
transformer.resblocks.10.mlp.c_fc.bias FLOAT[3072] dc5a235cd23d
transformer.resblocks.10.mlp.c_proj.bias FLOAT[768] fca583582108
transformer.resblocks.11.attn.in_proj_bias FLOAT[2304] 1238efe37956
transformer.resblocks.11.attn.out_proj.bias FLOAT[768] be1c46be8acc
transformer.resblocks.11.ln_1.bias FLOAT[768] 703f1909a44d
transformer.resblocks.11.ln_1.weight FLOAT[768] 8da1a86af565
transformer.resblocks.11.ln_2.bias FLOAT[768] e58a4584244c
transformer.resblocks.11.ln_2.weight FLOAT[768] c8c745a0c9f5
transformer.resblocks.11.mlp.c_fc.bias FLOAT[3072] 1be83aabcafe
transformer.resblocks.11.mlp.c_proj.bias FLOAT[768] 553ad2d09902
transformer.resblocks.2.attn.in_proj_bias FLOAT[2304] 6b6f4c6df638
transformer.resblocks.2.attn.out_proj.bias FLOAT[768] 20673f00f48e
transformer.resblocks.2.ln_1.bias FLOAT[768] 40e06fbee78c
transformer.resblocks.2.ln_1.weight FLOAT[768] 6f7066ec3b3b
transformer.resblocks.2.ln_2.bias FLOAT[768] 90c95ea442f6
transformer.resblocks.2.ln_2.weight FLOAT[768] a8d464390483
transformer.resblocks.2.mlp.c_fc.bias FLOAT[3072] b21eeb1a71df
transformer.resblocks.2.mlp.c_proj.bias FLOAT[768] 94455c01cfe1
transformer.resblocks.3.attn.in_proj_bias FLOAT[2304] 1ba57a07e715
transformer.resblocks.3.attn.out_proj.bias FLOAT[768] 7576149e51fd
transformer.resblocks.3.ln_1.bias FLOAT[768] 7a3fb3db1ae5
transformer.resblocks.3.ln_1.weight FLOAT[768] f4241418f2fc
transformer.resblocks.3.ln_2.bias FLOAT[768] 21d4f7b01d7b
transformer.resblocks.3.ln_2.weight FLOAT[768] 73adb99b0cb9
transformer.resblocks.3.mlp.c_fc.bias FLOAT[3072] 2ee327bc099a
transformer.resblocks.3.mlp.c_proj.bias FLOAT[768] cffb52185a45
transformer.resblocks.4.attn.in_proj_bias FLOAT[2304] 3a4b4cc4b235
transformer.resblocks.4.attn.out_proj.bias FLOAT[768] 3ae63fc9bdb5
transformer.resblocks.4.ln_1.bias FLOAT[768] 060e3d8bad0a
transformer.resblocks.4.ln_1.weight FLOAT[768] d1bf33fb4c76
transformer.resblocks.4.ln_2.bias FLOAT[768] 172d7f3c8751
transformer.resblocks.4.ln_2.weight FLOAT[768] 6a7a96a9decb
transformer.resblocks.4.mlp.c_fc.bias FLOAT[3072] 3c8fc61589c9
transformer.resblocks.4.mlp.c_proj.bias FLOAT[768] 9d7c390f4ea7
transformer.resblocks.5.attn.in_proj_bias FLOAT[2304] 6a524b933a36
transformer.resblocks.5.attn.out_proj.bias FLOAT[768] 5a9161bb9533
transformer.resblocks.5.ln_1.bias FLOAT[768] 1e9f698804d5
transformer.resblocks.5.ln_1.weight FLOAT[768] cd8563b08598
transformer.resblocks.5.ln_2.bias FLOAT[768] a21a662d5f19
transformer.resblocks.5.ln_2.weight FLOAT[768] 538a6ecd1318
transformer.resblocks.5.mlp.c_fc.bias FLOAT[3072] 4f858a93fd51
transformer.resblocks.5.mlp.c_proj.bias FLOAT[768] e15429213a03
transformer.resblocks.6.attn.in_proj_bias FLOAT[2304] 4d72c77bf2ce
transformer.resblocks.6.attn.out_proj.bias FLOAT[768] ea204f17e6af
transformer.resblocks.6.ln_1.bias FLOAT[768] bf9bb20f6aff
transformer.resblocks.6.ln_1.weight FLOAT[768] 6c5c44242938
transformer.resblocks.6.ln_2.bias FLOAT[768] 50a5b7c18b09
transformer.resblocks.6.ln_2.weight FLOAT[768] 0350b1dac9ed
transformer.resblocks.6.mlp.c_fc.bias FLOAT[3072] 581778bc039c
transformer.resblocks.6.mlp.c_proj.bias FLOAT[768] 20a5d3fe5057
transformer.resblocks.7.attn.in_proj_bias FLOAT[2304] d9c5d4a06613
transformer.resblocks.7.attn.out_proj.bias FLOAT[768] eba146c26b8e
transformer.resblocks.7.ln_1.bias FLOAT[768] d01a759ba3c9
transformer.resblocks.7.ln_1.weight FLOAT[768] 345cad3a1265
transformer.resblocks.7.ln_2.bias FLOAT[768] 0cb9356c26e0
transformer.resblocks.7.ln_2.weight FLOAT[768] 1f3e48680af5
transformer.resblocks.7.mlp.c_fc.bias FLOAT[3072] b9f87c33200a
transformer.resblocks.7.mlp.c_proj.bias FLOAT[768] 3b835d2fcd8d
transformer.resblocks.8.attn.in_proj_bias FLOAT[2304] 1acad7b55635
transformer.resblocks.8.attn.out_proj.bias FLOAT[768] 4b085c47eef1
transformer.resblocks.8.ln_1.bias FLOAT[768] d8224267ae98
transformer.resblocks.8.ln_1.weight FLOAT[768] 3f8d1bde3aca
transformer.resblocks.8.ln_2.bias FLOAT[768] 34a72cb44854
transformer.resblocks.8.ln_2.weight FLOAT[768] b0bba471fd51
transformer.resblocks.8.mlp.c_fc.bias FLOAT[3072] de3f9d47f5e1
transformer.resblocks.8.mlp.c_proj.bias FLOAT[768] f0d003f5a94b
transformer.resblocks.9.attn.in_proj_bias FLOAT[2304] 06518f1cd334
transformer.resblocks.9.attn.out_proj.bias FLOAT[768] a2b63fcbf47c
transformer.resblocks.9.ln_1.bias FLOAT[768] 6b9df3388ffd
transformer.resblocks.9.ln_1.weight FLOAT[768] 9ad39b8a7f34
transformer.resblocks.9.ln_2.bias FLOAT[768] 186dbcc9e4a5
transformer.resblocks.9.ln_2.weight FLOAT[768] 32f052b29716
transformer.resblocks.9.mlp.c_fc.bias FLOAT[3072] f500198e6370
transformer.resblocks.9.mlp.c_proj.bias FLOAT[768] bcd96cc7e6d7
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[3072,768] fe54acea429e
val_11 FLOAT[768,2304] 85c524a38422
val_12 FLOAT[768,3072] d120faa20a20
val_13 FLOAT[3072,768] 0d4d1d8f8c71
val_14 FLOAT[768,2304] f7ffbcce3ddb
val_15 FLOAT[768,3072] d42495f58759
val_16 FLOAT[3072,768] da4ffce089c3
val_17 FLOAT[768,2304] f474e1ca7890
val_18 FLOAT[768,3072] 027be79020cc
val_19 FLOAT[3072,768] cd351eabc99a
val_2 FLOAT[768,2304] ef3fc6c030dd
val_20 FLOAT[768,2304] 78ac4dae395f
val_21 FLOAT[768,3072] fa8bdfc3ab03
val_22 FLOAT[3072,768] cb2f4f19a369
val_23 FLOAT[768,2304] 7e17490b88ad
val_24 FLOAT[768,3072] a9ad1504f0e6
val_25 FLOAT[3072,768] 2b45215f725a
val_26 FLOAT[768,2304] 1693aed3a079
val_27 FLOAT[768,3072] 9eafdba7fffb
val_28 FLOAT[3072,768] 0b67022b1be6
val_29 FLOAT[768,2304] 79e2aee8a0e3
val_3 FLOAT[768,3072] a1071d1a17e6
val_30 FLOAT[768,3072] 589df4467fa2
val_31 FLOAT[3072,768] 4c5cf2b0cb96
val_32 FLOAT[768,2304] 750976e22af0
val_33 FLOAT[768,3072] 0d36a45752e0
val_34 FLOAT[3072,768] 6cf5730bec22
val_35 FLOAT[768,2304] 980d2d2dfb0a
val_36 FLOAT[768,3072] 1cc3aafe5324
val_37 FLOAT[3072,768] 0b5906653ce2
val_4 FLOAT[3072,768] 353c9f24e275
val_5 FLOAT[768,2304] 59b703214a17
val_6 FLOAT[768,3072] 4eae32e1c5f7
val_7 FLOAT[3072,768] 933d793850fd
val_755_axes1 INT64[1] 7c9fa136d441
val_755_eye FLOAT[77,77] 39ccea14e08c
val_8 FLOAT[768,2304] f265bfe82714
val_9 FLOAT[768,3072] 07e033a582be
