<
   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] df5297638f99
ln_final.weight FLOAT[512] 3404812580e2
node_scaled_dot_product_attention_10_wo_t FLOAT[512,512] 6a7a7526c1af
node_scaled_dot_product_attention_11_wo_t FLOAT[512,512] 57f3781aa4fe
node_scaled_dot_product_attention_1_wo_t FLOAT[512,512] d2d87b406424
node_scaled_dot_product_attention_2_wo_t FLOAT[512,512] 29767e5e85a4
node_scaled_dot_product_attention_3_wo_t FLOAT[512,512] 2a1ad5567af4
node_scaled_dot_product_attention_4_wo_t FLOAT[512,512] fc5226f9bc5f
node_scaled_dot_product_attention_5_wo_t FLOAT[512,512] 7543a2b0a008
node_scaled_dot_product_attention_6_wo_t FLOAT[512,512] bbdf1c8cabd6
node_scaled_dot_product_attention_7_wo_t FLOAT[512,512] 0182e11c47f4
node_scaled_dot_product_attention_8_wo_t FLOAT[512,512] 08ed08b71750
node_scaled_dot_product_attention_9_wo_t FLOAT[512,512] 6fcd6c679ce8
node_scaled_dot_product_attention_wo_t FLOAT[512,512] d041de2c7c2c
positional_embedding FLOAT[77,512] 3baa7b9d4fe9
text_projection FLOAT[512,512] af807e41ad49
token_embedding.weight_fp16 FLOAT16[49408,512] 4c784728015d
transformer.resblocks.0.attn.in_proj_bias FLOAT[1536] 398d5bbef923
transformer.resblocks.0.attn.out_proj.bias FLOAT[512] b6ee5f998bfb
transformer.resblocks.0.ln_1.bias FLOAT[512] 504fe800c4c2
transformer.resblocks.0.ln_1.weight FLOAT[512] 55c135edf109
transformer.resblocks.0.ln_2.bias FLOAT[512] c5ad3b6eca3e
transformer.resblocks.0.ln_2.weight FLOAT[512] 711bdaf89252
transformer.resblocks.0.mlp.c_fc.bias FLOAT[2048] e22601115007
transformer.resblocks.0.mlp.c_proj.bias FLOAT[512] fb36ca24177d
transformer.resblocks.1.attn.in_proj_bias FLOAT[1536] fca32520f96b
transformer.resblocks.1.attn.out_proj.bias FLOAT[512] be06b172dbc6
transformer.resblocks.1.ln_1.bias FLOAT[512] 115278405742
transformer.resblocks.1.ln_1.weight FLOAT[512] abeede809a1d
transformer.resblocks.1.ln_2.bias FLOAT[512] 1d037748c399
transformer.resblocks.1.ln_2.weight FLOAT[512] 2fe3e4d96eeb
transformer.resblocks.1.mlp.c_fc.bias FLOAT[2048] e4f1de72a7d0
transformer.resblocks.1.mlp.c_proj.bias FLOAT[512] 62928f55c12d
transformer.resblocks.10.attn.in_proj_bias FLOAT[1536] 0f35eb9b94fc
transformer.resblocks.10.attn.out_proj.bias FLOAT[512] 56944e0d71df
transformer.resblocks.10.ln_1.bias FLOAT[512] 4dc996cabcd6
transformer.resblocks.10.ln_1.weight FLOAT[512] 6dca1dfe571a
transformer.resblocks.10.ln_2.bias FLOAT[512] 26af835c8710
transformer.resblocks.10.ln_2.weight FLOAT[512] 04cbfebbd74e
transformer.resblocks.10.mlp.c_fc.bias FLOAT[2048] 9945efcb871c
transformer.resblocks.10.mlp.c_proj.bias FLOAT[512] 7ad332c26395
transformer.resblocks.11.attn.in_proj_bias FLOAT[1536] e172bb582cbb
transformer.resblocks.11.attn.out_proj.bias FLOAT[512] 9b3c07eb1557
transformer.resblocks.11.ln_1.bias FLOAT[512] cb861a9577d5
transformer.resblocks.11.ln_1.weight FLOAT[512] 27ed5a91e0df
transformer.resblocks.11.ln_2.bias FLOAT[512] bb2e5ed22909
transformer.resblocks.11.ln_2.weight FLOAT[512] f8531fa50cdf
transformer.resblocks.11.mlp.c_fc.bias FLOAT[2048] 5861d556660e
transformer.resblocks.11.mlp.c_proj.bias FLOAT[512] 4837e5c98371
transformer.resblocks.2.attn.in_proj_bias FLOAT[1536] 4e841a3c86b9
transformer.resblocks.2.attn.out_proj.bias FLOAT[512] fde4765e4762
transformer.resblocks.2.ln_1.bias FLOAT[512] 03b060576f44
transformer.resblocks.2.ln_1.weight FLOAT[512] 574d3843c375
transformer.resblocks.2.ln_2.bias FLOAT[512] 6fc2a9e29e60
transformer.resblocks.2.ln_2.weight FLOAT[512] b26507d84d2a
transformer.resblocks.2.mlp.c_fc.bias FLOAT[2048] ec5d23c19821
transformer.resblocks.2.mlp.c_proj.bias FLOAT[512] 80924b3a7d7e
transformer.resblocks.3.attn.in_proj_bias FLOAT[1536] 84051974a34a
transformer.resblocks.3.attn.out_proj.bias FLOAT[512] 48214a148da5
transformer.resblocks.3.ln_1.bias FLOAT[512] aa19c81a88c0
transformer.resblocks.3.ln_1.weight FLOAT[512] f4d81a796a14
transformer.resblocks.3.ln_2.bias FLOAT[512] 8eddbb4cfb73
transformer.resblocks.3.ln_2.weight FLOAT[512] 51809a7e748e
transformer.resblocks.3.mlp.c_fc.bias FLOAT[2048] ee426ecfe6c0
transformer.resblocks.3.mlp.c_proj.bias FLOAT[512] 7082381d47bc
transformer.resblocks.4.attn.in_proj_bias FLOAT[1536] ad8c78ca0dc4
transformer.resblocks.4.attn.out_proj.bias FLOAT[512] 1a4fc8fd96c7
transformer.resblocks.4.ln_1.bias FLOAT[512] e107e980c3b6
transformer.resblocks.4.ln_1.weight FLOAT[512] 78def23a938f
transformer.resblocks.4.ln_2.bias FLOAT[512] 3f084eda79f7
transformer.resblocks.4.ln_2.weight FLOAT[512] 26c1773189f9
transformer.resblocks.4.mlp.c_fc.bias FLOAT[2048] cf31ad14f38d
transformer.resblocks.4.mlp.c_proj.bias FLOAT[512] e72f239899de
transformer.resblocks.5.attn.in_proj_bias FLOAT[1536] 2516b49a1d9b
transformer.resblocks.5.attn.out_proj.bias FLOAT[512] 57802a3e9c19
transformer.resblocks.5.ln_1.bias FLOAT[512] 01d552d57911
transformer.resblocks.5.ln_1.weight FLOAT[512] 9402669cdf27
transformer.resblocks.5.ln_2.bias FLOAT[512] 37a2b0827f1b
transformer.resblocks.5.ln_2.weight FLOAT[512] 4b7a369c973c
transformer.resblocks.5.mlp.c_fc.bias FLOAT[2048] e0c26619d733
transformer.resblocks.5.mlp.c_proj.bias FLOAT[512] a340f2bebd9c
transformer.resblocks.6.attn.in_proj_bias FLOAT[1536] 5f2ad0e05023
transformer.resblocks.6.attn.out_proj.bias FLOAT[512] 76611b485791
transformer.resblocks.6.ln_1.bias FLOAT[512] eeaedc865560
transformer.resblocks.6.ln_1.weight FLOAT[512] b843b277cfd1
transformer.resblocks.6.ln_2.bias FLOAT[512] b70de77d4636
transformer.resblocks.6.ln_2.weight FLOAT[512] ca8de3e5a498
transformer.resblocks.6.mlp.c_fc.bias FLOAT[2048] a33a1031a456
transformer.resblocks.6.mlp.c_proj.bias FLOAT[512] c8e701244ad4
transformer.resblocks.7.attn.in_proj_bias FLOAT[1536] 27b7ab5b0c0a
transformer.resblocks.7.attn.out_proj.bias FLOAT[512] c09667a28397
transformer.resblocks.7.ln_1.bias FLOAT[512] 8ce68fb13f79
transformer.resblocks.7.ln_1.weight FLOAT[512] 61b0de288a89
transformer.resblocks.7.ln_2.bias FLOAT[512] a38b04aaa2e8
transformer.resblocks.7.ln_2.weight FLOAT[512] 06faac3e5536
transformer.resblocks.7.mlp.c_fc.bias FLOAT[2048] c88023cd5eb6
transformer.resblocks.7.mlp.c_proj.bias FLOAT[512] 4a1deddacab9
transformer.resblocks.8.attn.in_proj_bias FLOAT[1536] c934894f298d
transformer.resblocks.8.attn.out_proj.bias FLOAT[512] 037f8a993dd2
transformer.resblocks.8.ln_1.bias FLOAT[512] fedfb9623631
transformer.resblocks.8.ln_1.weight FLOAT[512] c45070cd77ca
transformer.resblocks.8.ln_2.bias FLOAT[512] 86185bef383f
transformer.resblocks.8.ln_2.weight FLOAT[512] 997d7368da64
transformer.resblocks.8.mlp.c_fc.bias FLOAT[2048] 449bf1e311d6
transformer.resblocks.8.mlp.c_proj.bias FLOAT[512] fd9834ba0bb7
transformer.resblocks.9.attn.in_proj_bias FLOAT[1536] d6d4213f704a
transformer.resblocks.9.attn.out_proj.bias FLOAT[512] c6d5cb8cd033
transformer.resblocks.9.ln_1.bias FLOAT[512] 2f744d6a9e98
transformer.resblocks.9.ln_1.weight FLOAT[512] 21d45260ad4e
transformer.resblocks.9.ln_2.bias FLOAT[512] a16e29855e7f
transformer.resblocks.9.ln_2.weight FLOAT[512] 0c53137ead2b
transformer.resblocks.9.mlp.c_fc.bias FLOAT[2048] 18cf1bc59b7e
transformer.resblocks.9.mlp.c_proj.bias FLOAT[512] 3c76ac816334
val_0 INT64[1] 12a3ae445661
val_1 FLOAT[] 6708d9be4956
val_10 FLOAT[2048,512] e56df071d38b
val_11 FLOAT[512,1536] 1480e04ca6c6
val_12 FLOAT[512,2048] dc6b0c1d5b26
val_13 FLOAT[2048,512] 9033f3f78ae2
val_14 FLOAT[512,1536] a13f220ecee2
val_15 FLOAT[512,2048] 22ca2ab7a588
val_16 FLOAT[2048,512] 41027b8a8491
val_17 FLOAT[512,1536] 36385e62122b
val_18 FLOAT[512,2048] 0e2e8ab8eaf8
val_19 FLOAT[2048,512] 8c1896a4b911
val_2 FLOAT[512,1536] 901a9e767321
val_20 FLOAT[512,1536] 552f28cfa6cb
val_21 FLOAT[512,2048] e8087ba27400
val_22 FLOAT[2048,512] 2bd883a349b4
val_23 FLOAT[512,1536] 372106397fdf
val_24 FLOAT[512,2048] 0aec1ee99352
val_25 FLOAT[2048,512] 2b4caaa5a0c5
val_26 FLOAT[512,1536] dc40256cf82a
val_27 FLOAT[512,2048] 29bb709b7951
val_28 FLOAT[2048,512] fda3bb3e7129
val_29 FLOAT[512,1536] e1012784cbc5
val_3 FLOAT[512,2048] 790b80dc3775
val_30 FLOAT[512,2048] e690475b30e7
val_31 FLOAT[2048,512] e7047af54237
val_32 FLOAT[512,1536] ace20266d8a8
val_33 FLOAT[512,2048] 4c089438f6d8
val_34 FLOAT[2048,512] 51134218304e
val_35 FLOAT[512,1536] d579c15a4fc3
val_36 FLOAT[512,2048] 0a8a2c8dab02
val_37 FLOAT[2048,512] d49839f870c0
val_4 FLOAT[2048,512] 0c7f2ec26951
val_5 FLOAT[512,1536] e3b427c9de83
val_6 FLOAT[512,2048] 9a5989c2a163
val_7 FLOAT[2048,512] 8f247c373e5d
val_755_axes1 INT64[1] 7c9fa136d441
val_755_eye FLOAT[77,77] 39ccea14e08c
val_8 FLOAT[512,1536] 1dbdba123df1
val_9 FLOAT[512,2048] c9dadd47945e
