diff --git "a/scores/Qwen3-30B-A3B-pruned-IQ4_NL.md" "b/scores/Qwen3-30B-A3B-pruned-IQ4_NL.md"
new file mode 100644--- /dev/null
+++ "b/scores/Qwen3-30B-A3B-pruned-IQ4_NL.md"
@@ -0,0 +1,1653 @@
+# Qwen3-30B-A3B-IQ4_NL.gguf - GGUF Internal File Dump
+
+- Endian: LITTLE endian
+
+## Key Value Metadata Store
+
+There are 45 key-value pairs in this file
+
+| POS | TYPE | Count | Key | Value |
+|----:|:---------|-------:|:------------------------------------------|:--------------------------------------------------------------------|
+| 1 | UINT32 | 1 | GGUF.version | 3 |
+| 2 | UINT64 | 1 | GGUF.tensor_count | 555 |
+| 3 | UINT64 | 1 | GGUF.kv_count | 42 |
+| 4 | STRING | 1 | general.architecture | `qwen3moe` |
+| 5 | STRING | 1 | general.type | `model` |
+| 6 | STRING | 1 | general.name | `Qwen3 30B A3B` |
+| 7 | STRING | 1 | general.basename | `Qwen3` |
+| 8 | STRING | 1 | general.size_label | `30B-A3B` |
+| 9 | STRING | 1 | general.license | `apache-2.0` |
+| 10 | STRING | 1 | general.license.link | `https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE` |
+| 11 | UINT32 | 1 | general.base_model.count | 1 |
+| 12 | STRING | 1 | general.base_model.0.name | `Qwen3 30B A3B Base` |
+| 13 | STRING | 1 | general.base_model.0.organization | `Qwen` |
+| 14 | STRING | 1 | general.base_model.0.repo_url | `https://huggingface.co/Qwen/Qwen3-30B-A3B-Base` |
+| 15 | [STRING] | 1 | general.tags | [ `text-generation` ] |
+| 16 | UINT32 | 1 | qwen3moe.context_length | 40960 |
+| 17 | UINT32 | 1 | qwen3moe.embedding_length | 2048 |
+| 18 | UINT32 | 1 | qwen3moe.feed_forward_length | 6144 |
+| 19 | UINT32 | 1 | qwen3moe.attention.head_count | 32 |
+| 20 | UINT32 | 1 | qwen3moe.attention.head_count_kv | 4 |
+| 21 | FLOAT32 | 1 | qwen3moe.rope.freq_base | 1000000.0 |
+| 22 | FLOAT32 | 1 | qwen3moe.attention.layer_norm_rms_epsilon | 1e-06 |
+| 23 | UINT32 | 1 | qwen3moe.expert_used_count | 8 |
+| 24 | UINT32 | 1 | qwen3moe.attention.key_length | 128 |
+| 25 | UINT32 | 1 | qwen3moe.attention.value_length | 128 |
+| 26 | UINT32 | 1 | qwen3moe.expert_count | 128 |
+| 27 | UINT32 | 1 | qwen3moe.expert_feed_forward_length | 768 |
+| 28 | STRING | 1 | tokenizer.ggml.model | `gpt2` |
+| 29 | STRING | 1 | tokenizer.ggml.pre | `qwen2` |
+| 30 | [STRING] | 151936 | tokenizer.ggml.tokens | [ `!`, `"`, `#`, `$`, `%`, ... ] |
+| 31 | [INT32] | 151936 | tokenizer.ggml.token_type | [ 1, 1, 1, 1, 1, 1, 1, ... ] |
+| 32 | [STRING] | 151387 | tokenizer.ggml.merges | [ `Ġ Ġ`, `ĠĠ ĠĠ`, `i n`, `Ġ t`, `ĠĠĠĠ ĠĠĠĠ`, ... ] |
+| 33 | UINT32 | 1 | tokenizer.ggml.eos_token_id | 151645 |
+| 34 | UINT32 | 1 | tokenizer.ggml.padding_token_id | 151643 |
+| 35 | UINT32 | 1 | tokenizer.ggml.bos_token_id | 151643 |
+| 36 | BOOL | 1 | tokenizer.ggml.add_bos_token | False |
+| 37 | STRING | 1 | tokenizer.chat_template | `{%- if tools %}{{- '<|im_`...`{%- endif %}{%- endif %}` |
+| 38 | UINT32 | 1 | general.quantization_version | 2 |
+| 39 | UINT32 | 1 | general.file_type | 25 |
+| 40 | BOOL | 1 | general.pruned | True |
+| 41 | UINT32 | 1 | qwen3moe.block_count | 46 |
+| 42 | STRING | 1 | quantize.imatrix.file | `./imatrix/imatrix-Qwen3-30B-A3B-medium.dat` |
+| 43 | STRING | 1 | quantize.imatrix.dataset | `../../datasets/imatrix/combined_all_medium.txt` |
+| 44 | INT32 | 1 | quantize.imatrix.entries_count | 385 |
+| 45 | INT32 | 1 | quantize.imatrix.chunks_count | 6946 |
+
+## Tensors Overview ~29B Elements
+
+Total number of elements in all tensors: 29285881344 Elements
+
+- [Qwen3-30B-A3B-IQ4\_NL.gguf - GGUF Internal File Dump](#qwen3-30b-a3b-iq4_nlgguf---gguf-internal-file-dump)
+ - [Key Value Metadata Store](#key-value-metadata-store)
+ - [Tensors Overview ~29B Elements](#tensors-overview-29b-elements)
+ - [Tensor Data Offset](#tensor-data-offset)
+ - [Base Tensor Group : ~622M Elements](#base-tensor-group--622m-elements)
+ - [Block 0 Tensor Group : ~623M Elements](#block-0-tensor-group--623m-elements)
+ - [Block 1 Tensor Group : ~623M Elements](#block-1-tensor-group--623m-elements)
+ - [Block 2 Tensor Group : ~623M Elements](#block-2-tensor-group--623m-elements)
+ - [Block 3 Tensor Group : ~623M Elements](#block-3-tensor-group--623m-elements)
+ - [Block 4 Tensor Group : ~623M Elements](#block-4-tensor-group--623m-elements)
+ - [Block 5 Tensor Group : ~623M Elements](#block-5-tensor-group--623m-elements)
+ - [Block 6 Tensor Group : ~623M Elements](#block-6-tensor-group--623m-elements)
+ - [Block 7 Tensor Group : ~623M Elements](#block-7-tensor-group--623m-elements)
+ - [Block 8 Tensor Group : ~623M Elements](#block-8-tensor-group--623m-elements)
+ - [Block 9 Tensor Group : ~623M Elements](#block-9-tensor-group--623m-elements)
+ - [Block 10 Tensor Group : ~623M Elements](#block-10-tensor-group--623m-elements)
+ - [Block 11 Tensor Group : ~623M Elements](#block-11-tensor-group--623m-elements)
+ - [Block 12 Tensor Group : ~623M Elements](#block-12-tensor-group--623m-elements)
+ - [Block 13 Tensor Group : ~623M Elements](#block-13-tensor-group--623m-elements)
+ - [Block 14 Tensor Group : ~623M Elements](#block-14-tensor-group--623m-elements)
+ - [Block 15 Tensor Group : ~623M Elements](#block-15-tensor-group--623m-elements)
+ - [Block 16 Tensor Group : ~623M Elements](#block-16-tensor-group--623m-elements)
+ - [Block 17 Tensor Group : ~623M Elements](#block-17-tensor-group--623m-elements)
+ - [Block 18 Tensor Group : ~623M Elements](#block-18-tensor-group--623m-elements)
+ - [Block 19 Tensor Group : ~623M Elements](#block-19-tensor-group--623m-elements)
+ - [Block 20 Tensor Group : ~623M Elements](#block-20-tensor-group--623m-elements)
+ - [Block 21 Tensor Group : ~623M Elements](#block-21-tensor-group--623m-elements)
+ - [Block 22 Tensor Group : ~623M Elements](#block-22-tensor-group--623m-elements)
+ - [Block 23 Tensor Group : ~623M Elements](#block-23-tensor-group--623m-elements)
+ - [Block 24 Tensor Group : ~623M Elements](#block-24-tensor-group--623m-elements)
+ - [Block 25 Tensor Group : ~623M Elements](#block-25-tensor-group--623m-elements)
+ - [Block 26 Tensor Group : ~623M Elements](#block-26-tensor-group--623m-elements)
+ - [Block 27 Tensor Group : ~623M Elements](#block-27-tensor-group--623m-elements)
+ - [Block 28 Tensor Group : ~623M Elements](#block-28-tensor-group--623m-elements)
+ - [Block 29 Tensor Group : ~623M Elements](#block-29-tensor-group--623m-elements)
+ - [Block 30 Tensor Group : ~623M Elements](#block-30-tensor-group--623m-elements)
+ - [Block 31 Tensor Group : ~623M Elements](#block-31-tensor-group--623m-elements)
+ - [Block 32 Tensor Group : ~623M Elements](#block-32-tensor-group--623m-elements)
+ - [Block 33 Tensor Group : ~623M Elements](#block-33-tensor-group--623m-elements)
+ - [Block 34 Tensor Group : ~623M Elements](#block-34-tensor-group--623m-elements)
+ - [Block 35 Tensor Group : ~623M Elements](#block-35-tensor-group--623m-elements)
+ - [Block 36 Tensor Group : ~623M Elements](#block-36-tensor-group--623m-elements)
+ - [Block 37 Tensor Group : ~623M Elements](#block-37-tensor-group--623m-elements)
+ - [Block 38 Tensor Group : ~623M Elements](#block-38-tensor-group--623m-elements)
+ - [Block 39 Tensor Group : ~623M Elements](#block-39-tensor-group--623m-elements)
+ - [Block 40 Tensor Group : ~623M Elements](#block-40-tensor-group--623m-elements)
+ - [Block 41 Tensor Group : ~623M Elements](#block-41-tensor-group--623m-elements)
+ - [Block 42 Tensor Group : ~623M Elements](#block-42-tensor-group--623m-elements)
+ - [Block 43 Tensor Group : ~623M Elements](#block-43-tensor-group--623m-elements)
+ - [Block 44 Tensor Group : ~623M Elements](#block-44-tensor-group--623m-elements)
+ - [Block 45 Tensor Group : ~623M Elements](#block-45-tensor-group--623m-elements)
+
+### Tensor Data Offset
+
+This table contains the offset and data segment relative to start of file
+
+| T_ID | Tensor Layer Name | Data Offset (B) | Data Size (B) |
+|-----:|:----------------------------|-----------------:|-----------------:|
+| 0 | output.weight | 0x5b12e0 | 0xa6ec000 |
+| 1 | output_norm.weight | 0xac9d2e0 | 0x2000 |
+| 2 | token_embd.weight | 0xac9f2e0 | 0x7f82800 |
+| 3 | blk.0.attn_k.weight | 0x12c21ae0 | 0x6e000 |
+| 4 | blk.0.attn_k_norm.weight | 0x12c8fae0 | 0x200 |
+| 5 | blk.0.attn_norm.weight | 0x12c8fce0 | 0x2000 |
+| 6 | blk.0.attn_output.weight | 0x12c91ce0 | 0x480000 |
+| 7 | blk.0.attn_q.weight | 0x13111ce0 | 0x370000 |
+| 8 | blk.0.attn_q_norm.weight | 0x13481ce0 | 0x200 |
+| 9 | blk.0.attn_v.weight | 0x13481ee0 | 0x88000 |
+| 10 | blk.0.ffn_down_exps.weight | 0x13509ee0 | 0x8400000 |
+| 11 | blk.0.ffn_gate_exps.weight | 0x1b909ee0 | 0x5280000 |
+| 12 | blk.0.ffn_gate_inp.weight | 0x20b89ee0 | 0x100000 |
+| 13 | blk.0.ffn_norm.weight | 0x20c89ee0 | 0x2000 |
+| 14 | blk.0.ffn_up_exps.weight | 0x20c8bee0 | 0x5280000 |
+| 15 | blk.1.attn_k.weight | 0x25f0bee0 | 0x6e000 |
+| 16 | blk.1.attn_k_norm.weight | 0x25f79ee0 | 0x200 |
+| 17 | blk.1.attn_norm.weight | 0x25f7a0e0 | 0x2000 |
+| 18 | blk.1.attn_output.weight | 0x25f7c0e0 | 0x480000 |
+| 19 | blk.1.attn_q.weight | 0x263fc0e0 | 0x370000 |
+| 20 | blk.1.attn_q_norm.weight | 0x2676c0e0 | 0x200 |
+| 21 | blk.1.attn_v.weight | 0x2676c2e0 | 0x88000 |
+| 22 | blk.1.ffn_down_exps.weight | 0x267f42e0 | 0x8400000 |
+| 23 | blk.1.ffn_gate_exps.weight | 0x2ebf42e0 | 0x5280000 |
+| 24 | blk.1.ffn_gate_inp.weight | 0x33e742e0 | 0x100000 |
+| 25 | blk.1.ffn_norm.weight | 0x33f742e0 | 0x2000 |
+| 26 | blk.1.ffn_up_exps.weight | 0x33f762e0 | 0x5280000 |
+| 27 | blk.2.attn_k.weight | 0x391f62e0 | 0x6e000 |
+| 28 | blk.2.attn_k_norm.weight | 0x392642e0 | 0x200 |
+| 29 | blk.2.attn_norm.weight | 0x392644e0 | 0x2000 |
+| 30 | blk.2.attn_output.weight | 0x392664e0 | 0x480000 |
+| 31 | blk.2.attn_q.weight | 0x396e64e0 | 0x370000 |
+| 32 | blk.2.attn_q_norm.weight | 0x39a564e0 | 0x200 |
+| 33 | blk.2.attn_v.weight | 0x39a566e0 | 0x88000 |
+| 34 | blk.2.ffn_down_exps.weight | 0x39ade6e0 | 0x8400000 |
+| 35 | blk.2.ffn_gate_exps.weight | 0x41ede6e0 | 0x5280000 |
+| 36 | blk.2.ffn_gate_inp.weight | 0x4715e6e0 | 0x100000 |
+| 37 | blk.2.ffn_norm.weight | 0x4725e6e0 | 0x2000 |
+| 38 | blk.2.ffn_up_exps.weight | 0x472606e0 | 0x5280000 |
+| 39 | blk.3.attn_k.weight | 0x4c4e06e0 | 0x6e000 |
+| 40 | blk.3.attn_k_norm.weight | 0x4c54e6e0 | 0x200 |
+| 41 | blk.3.attn_norm.weight | 0x4c54e8e0 | 0x2000 |
+| 42 | blk.3.attn_output.weight | 0x4c5508e0 | 0x480000 |
+| 43 | blk.3.attn_q.weight | 0x4c9d08e0 | 0x370000 |
+| 44 | blk.3.attn_q_norm.weight | 0x4cd408e0 | 0x200 |
+| 45 | blk.3.attn_v.weight | 0x4cd40ae0 | 0x88000 |
+| 46 | blk.3.ffn_down_exps.weight | 0x4cdc8ae0 | 0x8400000 |
+| 47 | blk.3.ffn_gate_exps.weight | 0x551c8ae0 | 0x5280000 |
+| 48 | blk.3.ffn_gate_inp.weight | 0x5a448ae0 | 0x100000 |
+| 49 | blk.3.ffn_norm.weight | 0x5a548ae0 | 0x2000 |
+| 50 | blk.3.ffn_up_exps.weight | 0x5a54aae0 | 0x5280000 |
+| 51 | blk.4.attn_k.weight | 0x5f7caae0 | 0x6e000 |
+| 52 | blk.4.attn_k_norm.weight | 0x5f838ae0 | 0x200 |
+| 53 | blk.4.attn_norm.weight | 0x5f838ce0 | 0x2000 |
+| 54 | blk.4.attn_output.weight | 0x5f83ace0 | 0x480000 |
+| 55 | blk.4.attn_q.weight | 0x5fcbace0 | 0x370000 |
+| 56 | blk.4.attn_q_norm.weight | 0x6002ace0 | 0x200 |
+| 57 | blk.4.attn_v.weight | 0x6002aee0 | 0x88000 |
+| 58 | blk.4.ffn_down_exps.weight | 0x600b2ee0 | 0x8400000 |
+| 59 | blk.4.ffn_gate_exps.weight | 0x684b2ee0 | 0x5280000 |
+| 60 | blk.4.ffn_gate_inp.weight | 0x6d732ee0 | 0x100000 |
+| 61 | blk.4.ffn_norm.weight | 0x6d832ee0 | 0x2000 |
+| 62 | blk.4.ffn_up_exps.weight | 0x6d834ee0 | 0x5280000 |
+| 63 | blk.5.attn_k.weight | 0x72ab4ee0 | 0x6e000 |
+| 64 | blk.5.attn_k_norm.weight | 0x72b22ee0 | 0x200 |
+| 65 | blk.5.attn_norm.weight | 0x72b230e0 | 0x2000 |
+| 66 | blk.5.attn_output.weight | 0x72b250e0 | 0x480000 |
+| 67 | blk.5.attn_q.weight | 0x72fa50e0 | 0x370000 |
+| 68 | blk.5.attn_q_norm.weight | 0x733150e0 | 0x200 |
+| 69 | blk.5.attn_v.weight | 0x733152e0 | 0x88000 |
+| 70 | blk.5.ffn_down_exps.weight | 0x7339d2e0 | 0x8400000 |
+| 71 | blk.5.ffn_gate_exps.weight | 0x7b79d2e0 | 0x5280000 |
+| 72 | blk.5.ffn_gate_inp.weight | 0x80a1d2e0 | 0x100000 |
+| 73 | blk.5.ffn_norm.weight | 0x80b1d2e0 | 0x2000 |
+| 74 | blk.5.ffn_up_exps.weight | 0x80b1f2e0 | 0x5280000 |
+| 75 | blk.6.attn_k.weight | 0x85d9f2e0 | 0x6e000 |
+| 76 | blk.6.attn_k_norm.weight | 0x85e0d2e0 | 0x200 |
+| 77 | blk.6.attn_norm.weight | 0x85e0d4e0 | 0x2000 |
+| 78 | blk.6.attn_output.weight | 0x85e0f4e0 | 0x480000 |
+| 79 | blk.6.attn_q.weight | 0x8628f4e0 | 0x370000 |
+| 80 | blk.6.attn_q_norm.weight | 0x865ff4e0 | 0x200 |
+| 81 | blk.6.attn_v.weight | 0x865ff6e0 | 0x88000 |
+| 82 | blk.6.ffn_down_exps.weight | 0x866876e0 | 0x8400000 |
+| 83 | blk.6.ffn_gate_exps.weight | 0x8ea876e0 | 0x5280000 |
+| 84 | blk.6.ffn_gate_inp.weight | 0x93d076e0 | 0x100000 |
+| 85 | blk.6.ffn_norm.weight | 0x93e076e0 | 0x2000 |
+| 86 | blk.6.ffn_up_exps.weight | 0x93e096e0 | 0x5280000 |
+| 87 | blk.7.attn_k.weight | 0x990896e0 | 0x6e000 |
+| 88 | blk.7.attn_k_norm.weight | 0x990f76e0 | 0x200 |
+| 89 | blk.7.attn_norm.weight | 0x990f78e0 | 0x2000 |
+| 90 | blk.7.attn_output.weight | 0x990f98e0 | 0x480000 |
+| 91 | blk.7.attn_q.weight | 0x995798e0 | 0x370000 |
+| 92 | blk.7.attn_q_norm.weight | 0x998e98e0 | 0x200 |
+| 93 | blk.7.attn_v.weight | 0x998e9ae0 | 0x88000 |
+| 94 | blk.7.ffn_down_exps.weight | 0x99971ae0 | 0x8400000 |
+| 95 | blk.7.ffn_gate_exps.weight | 0xa1d71ae0 | 0x5280000 |
+| 96 | blk.7.ffn_gate_inp.weight | 0xa6ff1ae0 | 0x100000 |
+| 97 | blk.7.ffn_norm.weight | 0xa70f1ae0 | 0x2000 |
+| 98 | blk.7.ffn_up_exps.weight | 0xa70f3ae0 | 0x5280000 |
+| 99 | blk.8.attn_k.weight | 0xac373ae0 | 0x6e000 |
+| 100 | blk.8.attn_k_norm.weight | 0xac3e1ae0 | 0x200 |
+| 101 | blk.8.attn_norm.weight | 0xac3e1ce0 | 0x2000 |
+| 102 | blk.8.attn_output.weight | 0xac3e3ce0 | 0x480000 |
+| 103 | blk.8.attn_q.weight | 0xac863ce0 | 0x370000 |
+| 104 | blk.8.attn_q_norm.weight | 0xacbd3ce0 | 0x200 |
+| 105 | blk.8.attn_v.weight | 0xacbd3ee0 | 0x88000 |
+| 106 | blk.8.ffn_down_exps.weight | 0xacc5bee0 | 0x8400000 |
+| 107 | blk.8.ffn_gate_exps.weight | 0xb505bee0 | 0x5280000 |
+| 108 | blk.8.ffn_gate_inp.weight | 0xba2dbee0 | 0x100000 |
+| 109 | blk.8.ffn_norm.weight | 0xba3dbee0 | 0x2000 |
+| 110 | blk.8.ffn_up_exps.weight | 0xba3ddee0 | 0x5280000 |
+| 111 | blk.9.attn_k.weight | 0xbf65dee0 | 0x6e000 |
+| 112 | blk.9.attn_k_norm.weight | 0xbf6cbee0 | 0x200 |
+| 113 | blk.9.attn_norm.weight | 0xbf6cc0e0 | 0x2000 |
+| 114 | blk.9.attn_output.weight | 0xbf6ce0e0 | 0x480000 |
+| 115 | blk.9.attn_q.weight | 0xbfb4e0e0 | 0x370000 |
+| 116 | blk.9.attn_q_norm.weight | 0xbfebe0e0 | 0x200 |
+| 117 | blk.9.attn_v.weight | 0xbfebe2e0 | 0x88000 |
+| 118 | blk.9.ffn_down_exps.weight | 0xbff462e0 | 0x8400000 |
+| 119 | blk.9.ffn_gate_exps.weight | 0xc83462e0 | 0x5280000 |
+| 120 | blk.9.ffn_gate_inp.weight | 0xcd5c62e0 | 0x100000 |
+| 121 | blk.9.ffn_norm.weight | 0xcd6c62e0 | 0x2000 |
+| 122 | blk.9.ffn_up_exps.weight | 0xcd6c82e0 | 0x5280000 |
+| 123 | blk.10.attn_k.weight | 0xd29482e0 | 0x6e000 |
+| 124 | blk.10.attn_k_norm.weight | 0xd29b62e0 | 0x200 |
+| 125 | blk.10.attn_norm.weight | 0xd29b64e0 | 0x2000 |
+| 126 | blk.10.attn_output.weight | 0xd29b84e0 | 0x480000 |
+| 127 | blk.10.attn_q.weight | 0xd2e384e0 | 0x370000 |
+| 128 | blk.10.attn_q_norm.weight | 0xd31a84e0 | 0x200 |
+| 129 | blk.10.attn_v.weight | 0xd31a86e0 | 0x88000 |
+| 130 | blk.10.ffn_down_exps.weight | 0xd32306e0 | 0x8400000 |
+| 131 | blk.10.ffn_gate_exps.weight | 0xdb6306e0 | 0x5280000 |
+| 132 | blk.10.ffn_gate_inp.weight | 0xe08b06e0 | 0x100000 |
+| 133 | blk.10.ffn_norm.weight | 0xe09b06e0 | 0x2000 |
+| 134 | blk.10.ffn_up_exps.weight | 0xe09b26e0 | 0x5280000 |
+| 135 | blk.11.attn_k.weight | 0xe5c326e0 | 0x6e000 |
+| 136 | blk.11.attn_k_norm.weight | 0xe5ca06e0 | 0x200 |
+| 137 | blk.11.attn_norm.weight | 0xe5ca08e0 | 0x2000 |
+| 138 | blk.11.attn_output.weight | 0xe5ca28e0 | 0x480000 |
+| 139 | blk.11.attn_q.weight | 0xe61228e0 | 0x370000 |
+| 140 | blk.11.attn_q_norm.weight | 0xe64928e0 | 0x200 |
+| 141 | blk.11.attn_v.weight | 0xe6492ae0 | 0x88000 |
+| 142 | blk.11.ffn_down_exps.weight | 0xe651aae0 | 0x8400000 |
+| 143 | blk.11.ffn_gate_exps.weight | 0xee91aae0 | 0x5280000 |
+| 144 | blk.11.ffn_gate_inp.weight | 0xf3b9aae0 | 0x100000 |
+| 145 | blk.11.ffn_norm.weight | 0xf3c9aae0 | 0x2000 |
+| 146 | blk.11.ffn_up_exps.weight | 0xf3c9cae0 | 0x5280000 |
+| 147 | blk.12.attn_k.weight | 0xf8f1cae0 | 0x6e000 |
+| 148 | blk.12.attn_k_norm.weight | 0xf8f8aae0 | 0x200 |
+| 149 | blk.12.attn_norm.weight | 0xf8f8ace0 | 0x2000 |
+| 150 | blk.12.attn_output.weight | 0xf8f8cce0 | 0x480000 |
+| 151 | blk.12.attn_q.weight | 0xf940cce0 | 0x370000 |
+| 152 | blk.12.attn_q_norm.weight | 0xf977cce0 | 0x200 |
+| 153 | blk.12.attn_v.weight | 0xf977cee0 | 0x88000 |
+| 154 | blk.12.ffn_down_exps.weight | 0xf9804ee0 | 0x8400000 |
+| 155 | blk.12.ffn_gate_exps.weight | 0x101c04ee0 | 0x5280000 |
+| 156 | blk.12.ffn_gate_inp.weight | 0x106e84ee0 | 0x100000 |
+| 157 | blk.12.ffn_norm.weight | 0x106f84ee0 | 0x2000 |
+| 158 | blk.12.ffn_up_exps.weight | 0x106f86ee0 | 0x5280000 |
+| 159 | blk.13.attn_k.weight | 0x10c206ee0 | 0x6e000 |
+| 160 | blk.13.attn_k_norm.weight | 0x10c274ee0 | 0x200 |
+| 161 | blk.13.attn_norm.weight | 0x10c2750e0 | 0x2000 |
+| 162 | blk.13.attn_output.weight | 0x10c2770e0 | 0x480000 |
+| 163 | blk.13.attn_q.weight | 0x10c6f70e0 | 0x370000 |
+| 164 | blk.13.attn_q_norm.weight | 0x10ca670e0 | 0x200 |
+| 165 | blk.13.attn_v.weight | 0x10ca672e0 | 0x88000 |
+| 166 | blk.13.ffn_down_exps.weight | 0x10caef2e0 | 0x8400000 |
+| 167 | blk.13.ffn_gate_exps.weight | 0x114eef2e0 | 0x5280000 |
+| 168 | blk.13.ffn_gate_inp.weight | 0x11a16f2e0 | 0x100000 |
+| 169 | blk.13.ffn_norm.weight | 0x11a26f2e0 | 0x2000 |
+| 170 | blk.13.ffn_up_exps.weight | 0x11a2712e0 | 0x5280000 |
+| 171 | blk.14.attn_k.weight | 0x11f4f12e0 | 0x6e000 |
+| 172 | blk.14.attn_k_norm.weight | 0x11f55f2e0 | 0x200 |
+| 173 | blk.14.attn_norm.weight | 0x11f55f4e0 | 0x2000 |
+| 174 | blk.14.attn_output.weight | 0x11f5614e0 | 0x480000 |
+| 175 | blk.14.attn_q.weight | 0x11f9e14e0 | 0x370000 |
+| 176 | blk.14.attn_q_norm.weight | 0x11fd514e0 | 0x200 |
+| 177 | blk.14.attn_v.weight | 0x11fd516e0 | 0x88000 |
+| 178 | blk.14.ffn_down_exps.weight | 0x11fdd96e0 | 0x8400000 |
+| 179 | blk.14.ffn_gate_exps.weight | 0x1281d96e0 | 0x5280000 |
+| 180 | blk.14.ffn_gate_inp.weight | 0x12d4596e0 | 0x100000 |
+| 181 | blk.14.ffn_norm.weight | 0x12d5596e0 | 0x2000 |
+| 182 | blk.14.ffn_up_exps.weight | 0x12d55b6e0 | 0x5280000 |
+| 183 | blk.15.attn_k.weight | 0x1327db6e0 | 0x6e000 |
+| 184 | blk.15.attn_k_norm.weight | 0x1328496e0 | 0x200 |
+| 185 | blk.15.attn_norm.weight | 0x1328498e0 | 0x2000 |
+| 186 | blk.15.attn_output.weight | 0x13284b8e0 | 0x480000 |
+| 187 | blk.15.attn_q.weight | 0x132ccb8e0 | 0x370000 |
+| 188 | blk.15.attn_q_norm.weight | 0x13303b8e0 | 0x200 |
+| 189 | blk.15.attn_v.weight | 0x13303bae0 | 0x88000 |
+| 190 | blk.15.ffn_down_exps.weight | 0x1330c3ae0 | 0x8400000 |
+| 191 | blk.15.ffn_gate_exps.weight | 0x13b4c3ae0 | 0x5280000 |
+| 192 | blk.15.ffn_gate_inp.weight | 0x140743ae0 | 0x100000 |
+| 193 | blk.15.ffn_norm.weight | 0x140843ae0 | 0x2000 |
+| 194 | blk.15.ffn_up_exps.weight | 0x140845ae0 | 0x5280000 |
+| 195 | blk.16.attn_k.weight | 0x145ac5ae0 | 0x6e000 |
+| 196 | blk.16.attn_k_norm.weight | 0x145b33ae0 | 0x200 |
+| 197 | blk.16.attn_norm.weight | 0x145b33ce0 | 0x2000 |
+| 198 | blk.16.attn_output.weight | 0x145b35ce0 | 0x480000 |
+| 199 | blk.16.attn_q.weight | 0x145fb5ce0 | 0x370000 |
+| 200 | blk.16.attn_q_norm.weight | 0x146325ce0 | 0x200 |
+| 201 | blk.16.attn_v.weight | 0x146325ee0 | 0x88000 |
+| 202 | blk.16.ffn_down_exps.weight | 0x1463adee0 | 0x8400000 |
+| 203 | blk.16.ffn_gate_exps.weight | 0x14e7adee0 | 0x5280000 |
+| 204 | blk.16.ffn_gate_inp.weight | 0x153a2dee0 | 0x100000 |
+| 205 | blk.16.ffn_norm.weight | 0x153b2dee0 | 0x2000 |
+| 206 | blk.16.ffn_up_exps.weight | 0x153b2fee0 | 0x5280000 |
+| 207 | blk.17.attn_k.weight | 0x158dafee0 | 0x6e000 |
+| 208 | blk.17.attn_k_norm.weight | 0x158e1dee0 | 0x200 |
+| 209 | blk.17.attn_norm.weight | 0x158e1e0e0 | 0x2000 |
+| 210 | blk.17.attn_output.weight | 0x158e200e0 | 0x480000 |
+| 211 | blk.17.attn_q.weight | 0x1592a00e0 | 0x370000 |
+| 212 | blk.17.attn_q_norm.weight | 0x1596100e0 | 0x200 |
+| 213 | blk.17.attn_v.weight | 0x1596102e0 | 0x88000 |
+| 214 | blk.17.ffn_down_exps.weight | 0x1596982e0 | 0x8400000 |
+| 215 | blk.17.ffn_gate_exps.weight | 0x161a982e0 | 0x5280000 |
+| 216 | blk.17.ffn_gate_inp.weight | 0x166d182e0 | 0x100000 |
+| 217 | blk.17.ffn_norm.weight | 0x166e182e0 | 0x2000 |
+| 218 | blk.17.ffn_up_exps.weight | 0x166e1a2e0 | 0x5280000 |
+| 219 | blk.18.attn_k.weight | 0x16c09a2e0 | 0x6e000 |
+| 220 | blk.18.attn_k_norm.weight | 0x16c1082e0 | 0x200 |
+| 221 | blk.18.attn_norm.weight | 0x16c1084e0 | 0x2000 |
+| 222 | blk.18.attn_output.weight | 0x16c10a4e0 | 0x480000 |
+| 223 | blk.18.attn_q.weight | 0x16c58a4e0 | 0x370000 |
+| 224 | blk.18.attn_q_norm.weight | 0x16c8fa4e0 | 0x200 |
+| 225 | blk.18.attn_v.weight | 0x16c8fa6e0 | 0x88000 |
+| 226 | blk.18.ffn_down_exps.weight | 0x16c9826e0 | 0x8400000 |
+| 227 | blk.18.ffn_gate_exps.weight | 0x174d826e0 | 0x6c00000 |
+| 228 | blk.18.ffn_gate_inp.weight | 0x17b9826e0 | 0x100000 |
+| 229 | blk.18.ffn_norm.weight | 0x17ba826e0 | 0x2000 |
+| 230 | blk.18.ffn_up_exps.weight | 0x17ba846e0 | 0x6c00000 |
+| 231 | blk.19.attn_k.weight | 0x1826846e0 | 0x6e000 |
+| 232 | blk.19.attn_k_norm.weight | 0x1826f26e0 | 0x200 |
+| 233 | blk.19.attn_norm.weight | 0x1826f28e0 | 0x2000 |
+| 234 | blk.19.attn_output.weight | 0x1826f48e0 | 0x480000 |
+| 235 | blk.19.attn_q.weight | 0x182b748e0 | 0x370000 |
+| 236 | blk.19.attn_q_norm.weight | 0x182ee48e0 | 0x200 |
+| 237 | blk.19.attn_v.weight | 0x182ee4ae0 | 0x88000 |
+| 238 | blk.19.ffn_down_exps.weight | 0x182f6cae0 | 0x8400000 |
+| 239 | blk.19.ffn_gate_exps.weight | 0x18b36cae0 | 0x5280000 |
+| 240 | blk.19.ffn_gate_inp.weight | 0x1905ecae0 | 0x100000 |
+| 241 | blk.19.ffn_norm.weight | 0x1906ecae0 | 0x2000 |
+| 242 | blk.19.ffn_up_exps.weight | 0x1906eeae0 | 0x5280000 |
+| 243 | blk.20.attn_k.weight | 0x19596eae0 | 0x6e000 |
+| 244 | blk.20.attn_k_norm.weight | 0x1959dcae0 | 0x200 |
+| 245 | blk.20.attn_norm.weight | 0x1959dcce0 | 0x2000 |
+| 246 | blk.20.attn_output.weight | 0x1959dece0 | 0x480000 |
+| 247 | blk.20.attn_q.weight | 0x195e5ece0 | 0x370000 |
+| 248 | blk.20.attn_q_norm.weight | 0x1961cece0 | 0x200 |
+| 249 | blk.20.attn_v.weight | 0x1961ceee0 | 0x88000 |
+| 250 | blk.20.ffn_down_exps.weight | 0x196256ee0 | 0x8400000 |
+| 251 | blk.20.ffn_gate_exps.weight | 0x19e656ee0 | 0x5280000 |
+| 252 | blk.20.ffn_gate_inp.weight | 0x1a38d6ee0 | 0x100000 |
+| 253 | blk.20.ffn_norm.weight | 0x1a39d6ee0 | 0x2000 |
+| 254 | blk.20.ffn_up_exps.weight | 0x1a39d8ee0 | 0x5280000 |
+| 255 | blk.21.attn_k.weight | 0x1a8c58ee0 | 0x6e000 |
+| 256 | blk.21.attn_k_norm.weight | 0x1a8cc6ee0 | 0x200 |
+| 257 | blk.21.attn_norm.weight | 0x1a8cc70e0 | 0x2000 |
+| 258 | blk.21.attn_output.weight | 0x1a8cc90e0 | 0x480000 |
+| 259 | blk.21.attn_q.weight | 0x1a91490e0 | 0x370000 |
+| 260 | blk.21.attn_q_norm.weight | 0x1a94b90e0 | 0x200 |
+| 261 | blk.21.attn_v.weight | 0x1a94b92e0 | 0x88000 |
+| 262 | blk.21.ffn_down_exps.weight | 0x1a95412e0 | 0x8400000 |
+| 263 | blk.21.ffn_gate_exps.weight | 0x1b19412e0 | 0x5280000 |
+| 264 | blk.21.ffn_gate_inp.weight | 0x1b6bc12e0 | 0x100000 |
+| 265 | blk.21.ffn_norm.weight | 0x1b6cc12e0 | 0x2000 |
+| 266 | blk.21.ffn_up_exps.weight | 0x1b6cc32e0 | 0x5280000 |
+| 267 | blk.22.attn_k.weight | 0x1bbf432e0 | 0x6e000 |
+| 268 | blk.22.attn_k_norm.weight | 0x1bbfb12e0 | 0x200 |
+| 269 | blk.22.attn_norm.weight | 0x1bbfb14e0 | 0x2000 |
+| 270 | blk.22.attn_output.weight | 0x1bbfb34e0 | 0x480000 |
+| 271 | blk.22.attn_q.weight | 0x1bc4334e0 | 0x370000 |
+| 272 | blk.22.attn_q_norm.weight | 0x1bc7a34e0 | 0x200 |
+| 273 | blk.22.attn_v.weight | 0x1bc7a36e0 | 0x88000 |
+| 274 | blk.22.ffn_down_exps.weight | 0x1bc82b6e0 | 0x8400000 |
+| 275 | blk.22.ffn_gate_exps.weight | 0x1c4c2b6e0 | 0x5280000 |
+| 276 | blk.22.ffn_gate_inp.weight | 0x1c9eab6e0 | 0x100000 |
+| 277 | blk.22.ffn_norm.weight | 0x1c9fab6e0 | 0x2000 |
+| 278 | blk.22.ffn_up_exps.weight | 0x1c9fad6e0 | 0x5280000 |
+| 279 | blk.23.attn_k.weight | 0x1cf22d6e0 | 0x6e000 |
+| 280 | blk.23.attn_k_norm.weight | 0x1cf29b6e0 | 0x200 |
+| 281 | blk.23.attn_norm.weight | 0x1cf29b8e0 | 0x2000 |
+| 282 | blk.23.attn_output.weight | 0x1cf29d8e0 | 0x480000 |
+| 283 | blk.23.attn_q.weight | 0x1cf71d8e0 | 0x370000 |
+| 284 | blk.23.attn_q_norm.weight | 0x1cfa8d8e0 | 0x200 |
+| 285 | blk.23.attn_v.weight | 0x1cfa8dae0 | 0x88000 |
+| 286 | blk.23.ffn_down_exps.weight | 0x1cfb15ae0 | 0x8400000 |
+| 287 | blk.23.ffn_gate_exps.weight | 0x1d7f15ae0 | 0x5280000 |
+| 288 | blk.23.ffn_gate_inp.weight | 0x1dd195ae0 | 0x100000 |
+| 289 | blk.23.ffn_norm.weight | 0x1dd295ae0 | 0x2000 |
+| 290 | blk.23.ffn_up_exps.weight | 0x1dd297ae0 | 0x5280000 |
+| 291 | blk.24.attn_k.weight | 0x1e2517ae0 | 0x90000 |
+| 292 | blk.24.attn_k_norm.weight | 0x1e25a7ae0 | 0x200 |
+| 293 | blk.24.attn_norm.weight | 0x1e25a7ce0 | 0x2000 |
+| 294 | blk.24.attn_output.weight | 0x1e25a9ce0 | 0x480000 |
+| 295 | blk.24.attn_q.weight | 0x1e2a29ce0 | 0x480000 |
+| 296 | blk.24.attn_q_norm.weight | 0x1e2ea9ce0 | 0x200 |
+| 297 | blk.24.attn_v.weight | 0x1e2ea9ee0 | 0x90000 |
+| 298 | blk.24.ffn_down_exps.weight | 0x1e2f39ee0 | 0x8400000 |
+| 299 | blk.24.ffn_gate_exps.weight | 0x1eb339ee0 | 0x5280000 |
+| 300 | blk.24.ffn_gate_inp.weight | 0x1f05b9ee0 | 0x100000 |
+| 301 | blk.24.ffn_norm.weight | 0x1f06b9ee0 | 0x2000 |
+| 302 | blk.24.ffn_up_exps.weight | 0x1f06bbee0 | 0x5280000 |
+| 303 | blk.25.attn_k.weight | 0x1f593bee0 | 0x90000 |
+| 304 | blk.25.attn_k_norm.weight | 0x1f59cbee0 | 0x200 |
+| 305 | blk.25.attn_norm.weight | 0x1f59cc0e0 | 0x2000 |
+| 306 | blk.25.attn_output.weight | 0x1f59ce0e0 | 0x480000 |
+| 307 | blk.25.attn_q.weight | 0x1f5e4e0e0 | 0x480000 |
+| 308 | blk.25.attn_q_norm.weight | 0x1f62ce0e0 | 0x200 |
+| 309 | blk.25.attn_v.weight | 0x1f62ce2e0 | 0x90000 |
+| 310 | blk.25.ffn_down_exps.weight | 0x1f635e2e0 | 0x8400000 |
+| 311 | blk.25.ffn_gate_exps.weight | 0x1fe75e2e0 | 0x6c00000 |
+| 312 | blk.25.ffn_gate_inp.weight | 0x20535e2e0 | 0x100000 |
+| 313 | blk.25.ffn_norm.weight | 0x20545e2e0 | 0x2000 |
+| 314 | blk.25.ffn_up_exps.weight | 0x2054602e0 | 0x6c00000 |
+| 315 | blk.26.attn_k.weight | 0x20c0602e0 | 0x90000 |
+| 316 | blk.26.attn_k_norm.weight | 0x20c0f02e0 | 0x200 |
+| 317 | blk.26.attn_norm.weight | 0x20c0f04e0 | 0x2000 |
+| 318 | blk.26.attn_output.weight | 0x20c0f24e0 | 0x480000 |
+| 319 | blk.26.attn_q.weight | 0x20c5724e0 | 0x480000 |
+| 320 | blk.26.attn_q_norm.weight | 0x20c9f24e0 | 0x200 |
+| 321 | blk.26.attn_v.weight | 0x20c9f26e0 | 0x90000 |
+| 322 | blk.26.ffn_down_exps.weight | 0x20ca826e0 | 0x8400000 |
+| 323 | blk.26.ffn_gate_exps.weight | 0x214e826e0 | 0x6c00000 |
+| 324 | blk.26.ffn_gate_inp.weight | 0x21ba826e0 | 0x100000 |
+| 325 | blk.26.ffn_norm.weight | 0x21bb826e0 | 0x2000 |
+| 326 | blk.26.ffn_up_exps.weight | 0x21bb846e0 | 0x6c00000 |
+| 327 | blk.27.attn_k.weight | 0x2227846e0 | 0x90000 |
+| 328 | blk.27.attn_k_norm.weight | 0x2228146e0 | 0x200 |
+| 329 | blk.27.attn_norm.weight | 0x2228148e0 | 0x2000 |
+| 330 | blk.27.attn_output.weight | 0x2228168e0 | 0x480000 |
+| 331 | blk.27.attn_q.weight | 0x222c968e0 | 0x480000 |
+| 332 | blk.27.attn_q_norm.weight | 0x2231168e0 | 0x200 |
+| 333 | blk.27.attn_v.weight | 0x223116ae0 | 0x90000 |
+| 334 | blk.27.ffn_down_exps.weight | 0x2231a6ae0 | 0x8400000 |
+| 335 | blk.27.ffn_gate_exps.weight | 0x22b5a6ae0 | 0x6c00000 |
+| 336 | blk.27.ffn_gate_inp.weight | 0x2321a6ae0 | 0x100000 |
+| 337 | blk.27.ffn_norm.weight | 0x2322a6ae0 | 0x2000 |
+| 338 | blk.27.ffn_up_exps.weight | 0x2322a8ae0 | 0x6c00000 |
+| 339 | blk.28.attn_k.weight | 0x238ea8ae0 | 0x90000 |
+| 340 | blk.28.attn_k_norm.weight | 0x238f38ae0 | 0x200 |
+| 341 | blk.28.attn_norm.weight | 0x238f38ce0 | 0x2000 |
+| 342 | blk.28.attn_output.weight | 0x238f3ace0 | 0x480000 |
+| 343 | blk.28.attn_q.weight | 0x2393bace0 | 0x480000 |
+| 344 | blk.28.attn_q_norm.weight | 0x23983ace0 | 0x200 |
+| 345 | blk.28.attn_v.weight | 0x23983aee0 | 0x90000 |
+| 346 | blk.28.ffn_down_exps.weight | 0x2398caee0 | 0x8400000 |
+| 347 | blk.28.ffn_gate_exps.weight | 0x241ccaee0 | 0x6c00000 |
+| 348 | blk.28.ffn_gate_inp.weight | 0x2488caee0 | 0x100000 |
+| 349 | blk.28.ffn_norm.weight | 0x2489caee0 | 0x2000 |
+| 350 | blk.28.ffn_up_exps.weight | 0x2489ccee0 | 0x6c00000 |
+| 351 | blk.29.attn_k.weight | 0x24f5ccee0 | 0x90000 |
+| 352 | blk.29.attn_k_norm.weight | 0x24f65cee0 | 0x200 |
+| 353 | blk.29.attn_norm.weight | 0x24f65d0e0 | 0x2000 |
+| 354 | blk.29.attn_output.weight | 0x24f65f0e0 | 0x480000 |
+| 355 | blk.29.attn_q.weight | 0x24fadf0e0 | 0x480000 |
+| 356 | blk.29.attn_q_norm.weight | 0x24ff5f0e0 | 0x200 |
+| 357 | blk.29.attn_v.weight | 0x24ff5f2e0 | 0x90000 |
+| 358 | blk.29.ffn_down_exps.weight | 0x24ffef2e0 | 0x8400000 |
+| 359 | blk.29.ffn_gate_exps.weight | 0x2583ef2e0 | 0x6c00000 |
+| 360 | blk.29.ffn_gate_inp.weight | 0x25efef2e0 | 0x100000 |
+| 361 | blk.29.ffn_norm.weight | 0x25f0ef2e0 | 0x2000 |
+| 362 | blk.29.ffn_up_exps.weight | 0x25f0f12e0 | 0x6c00000 |
+| 363 | blk.30.attn_k.weight | 0x265cf12e0 | 0x90000 |
+| 364 | blk.30.attn_k_norm.weight | 0x265d812e0 | 0x200 |
+| 365 | blk.30.attn_norm.weight | 0x265d814e0 | 0x2000 |
+| 366 | blk.30.attn_output.weight | 0x265d834e0 | 0x480000 |
+| 367 | blk.30.attn_q.weight | 0x2662034e0 | 0x480000 |
+| 368 | blk.30.attn_q_norm.weight | 0x2666834e0 | 0x200 |
+| 369 | blk.30.attn_v.weight | 0x2666836e0 | 0x90000 |
+| 370 | blk.30.ffn_down_exps.weight | 0x2667136e0 | 0x8400000 |
+| 371 | blk.30.ffn_gate_exps.weight | 0x26eb136e0 | 0x6c00000 |
+| 372 | blk.30.ffn_gate_inp.weight | 0x2757136e0 | 0x100000 |
+| 373 | blk.30.ffn_norm.weight | 0x2758136e0 | 0x2000 |
+| 374 | blk.30.ffn_up_exps.weight | 0x2758156e0 | 0x6c00000 |
+| 375 | blk.31.attn_k.weight | 0x27c4156e0 | 0x90000 |
+| 376 | blk.31.attn_k_norm.weight | 0x27c4a56e0 | 0x200 |
+| 377 | blk.31.attn_norm.weight | 0x27c4a58e0 | 0x2000 |
+| 378 | blk.31.attn_output.weight | 0x27c4a78e0 | 0x480000 |
+| 379 | blk.31.attn_q.weight | 0x27c9278e0 | 0x480000 |
+| 380 | blk.31.attn_q_norm.weight | 0x27cda78e0 | 0x200 |
+| 381 | blk.31.attn_v.weight | 0x27cda7ae0 | 0x90000 |
+| 382 | blk.31.ffn_down_exps.weight | 0x27ce37ae0 | 0x8400000 |
+| 383 | blk.31.ffn_gate_exps.weight | 0x285237ae0 | 0x6c00000 |
+| 384 | blk.31.ffn_gate_inp.weight | 0x28be37ae0 | 0x100000 |
+| 385 | blk.31.ffn_norm.weight | 0x28bf37ae0 | 0x2000 |
+| 386 | blk.31.ffn_up_exps.weight | 0x28bf39ae0 | 0x6c00000 |
+| 387 | blk.32.attn_k.weight | 0x292b39ae0 | 0x90000 |
+| 388 | blk.32.attn_k_norm.weight | 0x292bc9ae0 | 0x200 |
+| 389 | blk.32.attn_norm.weight | 0x292bc9ce0 | 0x2000 |
+| 390 | blk.32.attn_output.weight | 0x292bcbce0 | 0x480000 |
+| 391 | blk.32.attn_q.weight | 0x29304bce0 | 0x480000 |
+| 392 | blk.32.attn_q_norm.weight | 0x2934cbce0 | 0x200 |
+| 393 | blk.32.attn_v.weight | 0x2934cbee0 | 0x90000 |
+| 394 | blk.32.ffn_down_exps.weight | 0x29355bee0 | 0x8400000 |
+| 395 | blk.32.ffn_gate_exps.weight | 0x29b95bee0 | 0x6c00000 |
+| 396 | blk.32.ffn_gate_inp.weight | 0x2a255bee0 | 0x100000 |
+| 397 | blk.32.ffn_norm.weight | 0x2a265bee0 | 0x2000 |
+| 398 | blk.32.ffn_up_exps.weight | 0x2a265dee0 | 0x6c00000 |
+| 399 | blk.33.attn_k.weight | 0x2a925dee0 | 0x90000 |
+| 400 | blk.33.attn_k_norm.weight | 0x2a92edee0 | 0x200 |
+| 401 | blk.33.attn_norm.weight | 0x2a92ee0e0 | 0x2000 |
+| 402 | blk.33.attn_output.weight | 0x2a92f00e0 | 0x480000 |
+| 403 | blk.33.attn_q.weight | 0x2a97700e0 | 0x480000 |
+| 404 | blk.33.attn_q_norm.weight | 0x2a9bf00e0 | 0x200 |
+| 405 | blk.33.attn_v.weight | 0x2a9bf02e0 | 0x90000 |
+| 406 | blk.33.ffn_down_exps.weight | 0x2a9c802e0 | 0x8400000 |
+| 407 | blk.33.ffn_gate_exps.weight | 0x2b20802e0 | 0x6c00000 |
+| 408 | blk.33.ffn_gate_inp.weight | 0x2b8c802e0 | 0x100000 |
+| 409 | blk.33.ffn_norm.weight | 0x2b8d802e0 | 0x2000 |
+| 410 | blk.33.ffn_up_exps.weight | 0x2b8d822e0 | 0x6c00000 |
+| 411 | blk.34.attn_k.weight | 0x2bf9822e0 | 0x90000 |
+| 412 | blk.34.attn_k_norm.weight | 0x2bfa122e0 | 0x200 |
+| 413 | blk.34.attn_norm.weight | 0x2bfa124e0 | 0x2000 |
+| 414 | blk.34.attn_output.weight | 0x2bfa144e0 | 0x480000 |
+| 415 | blk.34.attn_q.weight | 0x2bfe944e0 | 0x480000 |
+| 416 | blk.34.attn_q_norm.weight | 0x2c03144e0 | 0x200 |
+| 417 | blk.34.attn_v.weight | 0x2c03146e0 | 0x90000 |
+| 418 | blk.34.ffn_down_exps.weight | 0x2c03a46e0 | 0x8400000 |
+| 419 | blk.34.ffn_gate_exps.weight | 0x2c87a46e0 | 0x6c00000 |
+| 420 | blk.34.ffn_gate_inp.weight | 0x2cf3a46e0 | 0x100000 |
+| 421 | blk.34.ffn_norm.weight | 0x2cf4a46e0 | 0x2000 |
+| 422 | blk.34.ffn_up_exps.weight | 0x2cf4a66e0 | 0x6c00000 |
+| 423 | blk.35.attn_k.weight | 0x2d60a66e0 | 0x90000 |
+| 424 | blk.35.attn_k_norm.weight | 0x2d61366e0 | 0x200 |
+| 425 | blk.35.attn_norm.weight | 0x2d61368e0 | 0x2000 |
+| 426 | blk.35.attn_output.weight | 0x2d61388e0 | 0x480000 |
+| 427 | blk.35.attn_q.weight | 0x2d65b88e0 | 0x480000 |
+| 428 | blk.35.attn_q_norm.weight | 0x2d6a388e0 | 0x200 |
+| 429 | blk.35.attn_v.weight | 0x2d6a38ae0 | 0x90000 |
+| 430 | blk.35.ffn_down_exps.weight | 0x2d6ac8ae0 | 0x8400000 |
+| 431 | blk.35.ffn_gate_exps.weight | 0x2deec8ae0 | 0x6c00000 |
+| 432 | blk.35.ffn_gate_inp.weight | 0x2e5ac8ae0 | 0x100000 |
+| 433 | blk.35.ffn_norm.weight | 0x2e5bc8ae0 | 0x2000 |
+| 434 | blk.35.ffn_up_exps.weight | 0x2e5bcaae0 | 0x6c00000 |
+| 435 | blk.36.attn_k.weight | 0x2ec7caae0 | 0x90000 |
+| 436 | blk.36.attn_k_norm.weight | 0x2ec85aae0 | 0x200 |
+| 437 | blk.36.attn_norm.weight | 0x2ec85ace0 | 0x2000 |
+| 438 | blk.36.attn_output.weight | 0x2ec85cce0 | 0x480000 |
+| 439 | blk.36.attn_q.weight | 0x2eccdcce0 | 0x480000 |
+| 440 | blk.36.attn_q_norm.weight | 0x2ed15cce0 | 0x200 |
+| 441 | blk.36.attn_v.weight | 0x2ed15cee0 | 0x90000 |
+| 442 | blk.36.ffn_down_exps.weight | 0x2ed1ecee0 | 0x8400000 |
+| 443 | blk.36.ffn_gate_exps.weight | 0x2f55ecee0 | 0x6c00000 |
+| 444 | blk.36.ffn_gate_inp.weight | 0x2fc1ecee0 | 0x100000 |
+| 445 | blk.36.ffn_norm.weight | 0x2fc2ecee0 | 0x2000 |
+| 446 | blk.36.ffn_up_exps.weight | 0x2fc2eeee0 | 0x6c00000 |
+| 447 | blk.37.attn_k.weight | 0x302eeeee0 | 0x90000 |
+| 448 | blk.37.attn_k_norm.weight | 0x302f7eee0 | 0x200 |
+| 449 | blk.37.attn_norm.weight | 0x302f7f0e0 | 0x2000 |
+| 450 | blk.37.attn_output.weight | 0x302f810e0 | 0x480000 |
+| 451 | blk.37.attn_q.weight | 0x3034010e0 | 0x480000 |
+| 452 | blk.37.attn_q_norm.weight | 0x3038810e0 | 0x200 |
+| 453 | blk.37.attn_v.weight | 0x3038812e0 | 0x90000 |
+| 454 | blk.37.ffn_down_exps.weight | 0x3039112e0 | 0x8400000 |
+| 455 | blk.37.ffn_gate_exps.weight | 0x30bd112e0 | 0x6c00000 |
+| 456 | blk.37.ffn_gate_inp.weight | 0x3129112e0 | 0x100000 |
+| 457 | blk.37.ffn_norm.weight | 0x312a112e0 | 0x2000 |
+| 458 | blk.37.ffn_up_exps.weight | 0x312a132e0 | 0x6c00000 |
+| 459 | blk.38.attn_k.weight | 0x3196132e0 | 0x90000 |
+| 460 | blk.38.attn_k_norm.weight | 0x3196a32e0 | 0x200 |
+| 461 | blk.38.attn_norm.weight | 0x3196a34e0 | 0x2000 |
+| 462 | blk.38.attn_output.weight | 0x3196a54e0 | 0x480000 |
+| 463 | blk.38.attn_q.weight | 0x319b254e0 | 0x480000 |
+| 464 | blk.38.attn_q_norm.weight | 0x319fa54e0 | 0x200 |
+| 465 | blk.38.attn_v.weight | 0x319fa56e0 | 0x90000 |
+| 466 | blk.38.ffn_down_exps.weight | 0x31a0356e0 | 0x8400000 |
+| 467 | blk.38.ffn_gate_exps.weight | 0x3224356e0 | 0x6c00000 |
+| 468 | blk.38.ffn_gate_inp.weight | 0x3290356e0 | 0x100000 |
+| 469 | blk.38.ffn_norm.weight | 0x3291356e0 | 0x2000 |
+| 470 | blk.38.ffn_up_exps.weight | 0x3291376e0 | 0x6c00000 |
+| 471 | blk.39.attn_k.weight | 0x32fd376e0 | 0x90000 |
+| 472 | blk.39.attn_k_norm.weight | 0x32fdc76e0 | 0x200 |
+| 473 | blk.39.attn_norm.weight | 0x32fdc78e0 | 0x2000 |
+| 474 | blk.39.attn_output.weight | 0x32fdc98e0 | 0x480000 |
+| 475 | blk.39.attn_q.weight | 0x3302498e0 | 0x480000 |
+| 476 | blk.39.attn_q_norm.weight | 0x3306c98e0 | 0x200 |
+| 477 | blk.39.attn_v.weight | 0x3306c9ae0 | 0x90000 |
+| 478 | blk.39.ffn_down_exps.weight | 0x330759ae0 | 0x8400000 |
+| 479 | blk.39.ffn_gate_exps.weight | 0x338b59ae0 | 0x6c00000 |
+| 480 | blk.39.ffn_gate_inp.weight | 0x33f759ae0 | 0x100000 |
+| 481 | blk.39.ffn_norm.weight | 0x33f859ae0 | 0x2000 |
+| 482 | blk.39.ffn_up_exps.weight | 0x33f85bae0 | 0x6c00000 |
+| 483 | blk.40.attn_k.weight | 0x34645bae0 | 0x90000 |
+| 484 | blk.40.attn_k_norm.weight | 0x3464ebae0 | 0x200 |
+| 485 | blk.40.attn_norm.weight | 0x3464ebce0 | 0x2000 |
+| 486 | blk.40.attn_output.weight | 0x3464edce0 | 0x480000 |
+| 487 | blk.40.attn_q.weight | 0x34696dce0 | 0x480000 |
+| 488 | blk.40.attn_q_norm.weight | 0x346dedce0 | 0x200 |
+| 489 | blk.40.attn_v.weight | 0x346dedee0 | 0x90000 |
+| 490 | blk.40.ffn_down_exps.weight | 0x346e7dee0 | 0x8400000 |
+| 491 | blk.40.ffn_gate_exps.weight | 0x34f27dee0 | 0x6c00000 |
+| 492 | blk.40.ffn_gate_inp.weight | 0x355e7dee0 | 0x100000 |
+| 493 | blk.40.ffn_norm.weight | 0x355f7dee0 | 0x2000 |
+| 494 | blk.40.ffn_up_exps.weight | 0x355f7fee0 | 0x6c00000 |
+| 495 | blk.41.attn_k.weight | 0x35cb7fee0 | 0x90000 |
+| 496 | blk.41.attn_k_norm.weight | 0x35cc0fee0 | 0x200 |
+| 497 | blk.41.attn_norm.weight | 0x35cc100e0 | 0x2000 |
+| 498 | blk.41.attn_output.weight | 0x35cc120e0 | 0x480000 |
+| 499 | blk.41.attn_q.weight | 0x35d0920e0 | 0x480000 |
+| 500 | blk.41.attn_q_norm.weight | 0x35d5120e0 | 0x200 |
+| 501 | blk.41.attn_v.weight | 0x35d5122e0 | 0x90000 |
+| 502 | blk.41.ffn_down_exps.weight | 0x35d5a22e0 | 0x8400000 |
+| 503 | blk.41.ffn_gate_exps.weight | 0x3659a22e0 | 0x6c00000 |
+| 504 | blk.41.ffn_gate_inp.weight | 0x36c5a22e0 | 0x100000 |
+| 505 | blk.41.ffn_norm.weight | 0x36c6a22e0 | 0x2000 |
+| 506 | blk.41.ffn_up_exps.weight | 0x36c6a42e0 | 0x6c00000 |
+| 507 | blk.42.attn_k.weight | 0x3732a42e0 | 0x90000 |
+| 508 | blk.42.attn_k_norm.weight | 0x3733342e0 | 0x200 |
+| 509 | blk.42.attn_norm.weight | 0x3733344e0 | 0x2000 |
+| 510 | blk.42.attn_output.weight | 0x3733364e0 | 0x480000 |
+| 511 | blk.42.attn_q.weight | 0x3737b64e0 | 0x480000 |
+| 512 | blk.42.attn_q_norm.weight | 0x373c364e0 | 0x200 |
+| 513 | blk.42.attn_v.weight | 0x373c366e0 | 0x90000 |
+| 514 | blk.42.ffn_down_exps.weight | 0x373cc66e0 | 0x8400000 |
+| 515 | blk.42.ffn_gate_exps.weight | 0x37c0c66e0 | 0x6c00000 |
+| 516 | blk.42.ffn_gate_inp.weight | 0x382cc66e0 | 0x100000 |
+| 517 | blk.42.ffn_norm.weight | 0x382dc66e0 | 0x2000 |
+| 518 | blk.42.ffn_up_exps.weight | 0x382dc86e0 | 0x6c00000 |
+| 519 | blk.43.attn_k.weight | 0x3899c86e0 | 0x90000 |
+| 520 | blk.43.attn_k_norm.weight | 0x389a586e0 | 0x200 |
+| 521 | blk.43.attn_norm.weight | 0x389a588e0 | 0x2000 |
+| 522 | blk.43.attn_output.weight | 0x389a5a8e0 | 0x480000 |
+| 523 | blk.43.attn_q.weight | 0x389eda8e0 | 0x480000 |
+| 524 | blk.43.attn_q_norm.weight | 0x38a35a8e0 | 0x200 |
+| 525 | blk.43.attn_v.weight | 0x38a35aae0 | 0x90000 |
+| 526 | blk.43.ffn_down_exps.weight | 0x38a3eaae0 | 0x8400000 |
+| 527 | blk.43.ffn_gate_exps.weight | 0x3927eaae0 | 0x6c00000 |
+| 528 | blk.43.ffn_gate_inp.weight | 0x3993eaae0 | 0x100000 |
+| 529 | blk.43.ffn_norm.weight | 0x3994eaae0 | 0x2000 |
+| 530 | blk.43.ffn_up_exps.weight | 0x3994ecae0 | 0x6c00000 |
+| 531 | blk.44.attn_k.weight | 0x3a00ecae0 | 0x90000 |
+| 532 | blk.44.attn_k_norm.weight | 0x3a017cae0 | 0x200 |
+| 533 | blk.44.attn_norm.weight | 0x3a017cce0 | 0x2000 |
+| 534 | blk.44.attn_output.weight | 0x3a017ece0 | 0x480000 |
+| 535 | blk.44.attn_q.weight | 0x3a05fece0 | 0x480000 |
+| 536 | blk.44.attn_q_norm.weight | 0x3a0a7ece0 | 0x200 |
+| 537 | blk.44.attn_v.weight | 0x3a0a7eee0 | 0x90000 |
+| 538 | blk.44.ffn_down_exps.weight | 0x3a0b0eee0 | 0x8400000 |
+| 539 | blk.44.ffn_gate_exps.weight | 0x3a8f0eee0 | 0x6c00000 |
+| 540 | blk.44.ffn_gate_inp.weight | 0x3afb0eee0 | 0x100000 |
+| 541 | blk.44.ffn_norm.weight | 0x3afc0eee0 | 0x2000 |
+| 542 | blk.44.ffn_up_exps.weight | 0x3afc10ee0 | 0x6c00000 |
+| 543 | blk.45.attn_k.weight | 0x3b6810ee0 | 0x90000 |
+| 544 | blk.45.attn_k_norm.weight | 0x3b68a0ee0 | 0x200 |
+| 545 | blk.45.attn_norm.weight | 0x3b68a10e0 | 0x2000 |
+| 546 | blk.45.attn_output.weight | 0x3b68a30e0 | 0x480000 |
+| 547 | blk.45.attn_q.weight | 0x3b6d230e0 | 0x480000 |
+| 548 | blk.45.attn_q_norm.weight | 0x3b71a30e0 | 0x200 |
+| 549 | blk.45.attn_v.weight | 0x3b71a32e0 | 0x90000 |
+| 550 | blk.45.ffn_down_exps.weight | 0x3b72332e0 | 0x8400000 |
+| 551 | blk.45.ffn_gate_exps.weight | 0x3bf6332e0 | 0x6c00000 |
+| 552 | blk.45.ffn_gate_inp.weight | 0x3c62332e0 | 0x100000 |
+| 553 | blk.45.ffn_norm.weight | 0x3c63332e0 | 0x2000 |
+| 554 | blk.45.ffn_up_exps.weight | 0x3c63352e0 | 0x6c00000 |
+
+### Base Tensor Group : ~622M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:-------------------|:---------------------------------|:------------------|:----------------------|:-------|
+| 0 | output.weight | Output (W) | (~311M) 311164928 | 2048 x 151936 x 1 x 1 | IQ4_NL |
+| 1 | output_norm.weight | Output Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 2 | token_embd.weight | Token Embedding (W) | (~311M) 311164928 | 2048 x 151936 x 1 x 1 | IQ3_S |
+
+- Total elements in base: (~622M) 622331904
+- Percentage of total elements: 2.13%
+
+
+### Block 0 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 3 | blk.0.attn_k.weight | Block 0 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 4 | blk.0.attn_k_norm.weight | Block 0 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 5 | blk.0.attn_norm.weight | Block 0 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 6 | blk.0.attn_output.weight | Block 0 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 7 | blk.0.attn_q.weight | Block 0 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 8 | blk.0.attn_q_norm.weight | Block 0 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 9 | blk.0.attn_v.weight | Block 0 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 10 | blk.0.ffn_down_exps.weight | Block 0 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 11 | blk.0.ffn_gate_exps.weight | Block 0 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 12 | blk.0.ffn_gate_inp.weight | Block 0 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 13 | blk.0.ffn_norm.weight | Block 0 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 14 | blk.0.ffn_up_exps.weight | Block 0 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.0: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 1 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 15 | blk.1.attn_k.weight | Block 1 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 16 | blk.1.attn_k_norm.weight | Block 1 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 17 | blk.1.attn_norm.weight | Block 1 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 18 | blk.1.attn_output.weight | Block 1 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 19 | blk.1.attn_q.weight | Block 1 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 20 | blk.1.attn_q_norm.weight | Block 1 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 21 | blk.1.attn_v.weight | Block 1 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 22 | blk.1.ffn_down_exps.weight | Block 1 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 23 | blk.1.ffn_gate_exps.weight | Block 1 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 24 | blk.1.ffn_gate_inp.weight | Block 1 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 25 | blk.1.ffn_norm.weight | Block 1 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 26 | blk.1.ffn_up_exps.weight | Block 1 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.1: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 2 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 27 | blk.2.attn_k.weight | Block 2 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 28 | blk.2.attn_k_norm.weight | Block 2 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 29 | blk.2.attn_norm.weight | Block 2 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 30 | blk.2.attn_output.weight | Block 2 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 31 | blk.2.attn_q.weight | Block 2 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 32 | blk.2.attn_q_norm.weight | Block 2 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 33 | blk.2.attn_v.weight | Block 2 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 34 | blk.2.ffn_down_exps.weight | Block 2 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 35 | blk.2.ffn_gate_exps.weight | Block 2 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 36 | blk.2.ffn_gate_inp.weight | Block 2 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 37 | blk.2.ffn_norm.weight | Block 2 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 38 | blk.2.ffn_up_exps.weight | Block 2 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.2: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 3 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 39 | blk.3.attn_k.weight | Block 3 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 40 | blk.3.attn_k_norm.weight | Block 3 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 41 | blk.3.attn_norm.weight | Block 3 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 42 | blk.3.attn_output.weight | Block 3 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 43 | blk.3.attn_q.weight | Block 3 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 44 | blk.3.attn_q_norm.weight | Block 3 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 45 | blk.3.attn_v.weight | Block 3 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 46 | blk.3.ffn_down_exps.weight | Block 3 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 47 | blk.3.ffn_gate_exps.weight | Block 3 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 48 | blk.3.ffn_gate_inp.weight | Block 3 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 49 | blk.3.ffn_norm.weight | Block 3 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 50 | blk.3.ffn_up_exps.weight | Block 3 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.3: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 4 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 51 | blk.4.attn_k.weight | Block 4 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 52 | blk.4.attn_k_norm.weight | Block 4 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 53 | blk.4.attn_norm.weight | Block 4 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 54 | blk.4.attn_output.weight | Block 4 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 55 | blk.4.attn_q.weight | Block 4 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 56 | blk.4.attn_q_norm.weight | Block 4 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 57 | blk.4.attn_v.weight | Block 4 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 58 | blk.4.ffn_down_exps.weight | Block 4 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 59 | blk.4.ffn_gate_exps.weight | Block 4 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 60 | blk.4.ffn_gate_inp.weight | Block 4 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 61 | blk.4.ffn_norm.weight | Block 4 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 62 | blk.4.ffn_up_exps.weight | Block 4 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.4: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 5 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 63 | blk.5.attn_k.weight | Block 5 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 64 | blk.5.attn_k_norm.weight | Block 5 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 65 | blk.5.attn_norm.weight | Block 5 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 66 | blk.5.attn_output.weight | Block 5 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 67 | blk.5.attn_q.weight | Block 5 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 68 | blk.5.attn_q_norm.weight | Block 5 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 69 | blk.5.attn_v.weight | Block 5 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 70 | blk.5.ffn_down_exps.weight | Block 5 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 71 | blk.5.ffn_gate_exps.weight | Block 5 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 72 | blk.5.ffn_gate_inp.weight | Block 5 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 73 | blk.5.ffn_norm.weight | Block 5 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 74 | blk.5.ffn_up_exps.weight | Block 5 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.5: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 6 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 75 | blk.6.attn_k.weight | Block 6 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 76 | blk.6.attn_k_norm.weight | Block 6 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 77 | blk.6.attn_norm.weight | Block 6 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 78 | blk.6.attn_output.weight | Block 6 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 79 | blk.6.attn_q.weight | Block 6 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 80 | blk.6.attn_q_norm.weight | Block 6 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 81 | blk.6.attn_v.weight | Block 6 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 82 | blk.6.ffn_down_exps.weight | Block 6 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 83 | blk.6.ffn_gate_exps.weight | Block 6 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 84 | blk.6.ffn_gate_inp.weight | Block 6 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 85 | blk.6.ffn_norm.weight | Block 6 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 86 | blk.6.ffn_up_exps.weight | Block 6 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.6: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 7 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 87 | blk.7.attn_k.weight | Block 7 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 88 | blk.7.attn_k_norm.weight | Block 7 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 89 | blk.7.attn_norm.weight | Block 7 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 90 | blk.7.attn_output.weight | Block 7 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 91 | blk.7.attn_q.weight | Block 7 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 92 | blk.7.attn_q_norm.weight | Block 7 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 93 | blk.7.attn_v.weight | Block 7 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 94 | blk.7.ffn_down_exps.weight | Block 7 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 95 | blk.7.ffn_gate_exps.weight | Block 7 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 96 | blk.7.ffn_gate_inp.weight | Block 7 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 97 | blk.7.ffn_norm.weight | Block 7 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 98 | blk.7.ffn_up_exps.weight | Block 7 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.7: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 8 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 99 | blk.8.attn_k.weight | Block 8 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 100 | blk.8.attn_k_norm.weight | Block 8 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 101 | blk.8.attn_norm.weight | Block 8 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 102 | blk.8.attn_output.weight | Block 8 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 103 | blk.8.attn_q.weight | Block 8 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 104 | blk.8.attn_q_norm.weight | Block 8 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 105 | blk.8.attn_v.weight | Block 8 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 106 | blk.8.ffn_down_exps.weight | Block 8 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 107 | blk.8.ffn_gate_exps.weight | Block 8 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 108 | blk.8.ffn_gate_inp.weight | Block 8 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 109 | blk.8.ffn_norm.weight | Block 8 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 110 | blk.8.ffn_up_exps.weight | Block 8 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.8: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 9 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 111 | blk.9.attn_k.weight | Block 9 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 112 | blk.9.attn_k_norm.weight | Block 9 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 113 | blk.9.attn_norm.weight | Block 9 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 114 | blk.9.attn_output.weight | Block 9 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 115 | blk.9.attn_q.weight | Block 9 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 116 | blk.9.attn_q_norm.weight | Block 9 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 117 | blk.9.attn_v.weight | Block 9 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 118 | blk.9.ffn_down_exps.weight | Block 9 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 119 | blk.9.ffn_gate_exps.weight | Block 9 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 120 | blk.9.ffn_gate_inp.weight | Block 9 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 121 | blk.9.ffn_norm.weight | Block 9 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 122 | blk.9.ffn_up_exps.weight | Block 9 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.9: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 10 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 123 | blk.10.attn_k.weight | Block 10 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 124 | blk.10.attn_k_norm.weight | Block 10 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 125 | blk.10.attn_norm.weight | Block 10 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 126 | blk.10.attn_output.weight | Block 10 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 127 | blk.10.attn_q.weight | Block 10 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 128 | blk.10.attn_q_norm.weight | Block 10 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 129 | blk.10.attn_v.weight | Block 10 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 130 | blk.10.ffn_down_exps.weight | Block 10 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 131 | blk.10.ffn_gate_exps.weight | Block 10 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 132 | blk.10.ffn_gate_inp.weight | Block 10 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 133 | blk.10.ffn_norm.weight | Block 10 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 134 | blk.10.ffn_up_exps.weight | Block 10 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.10: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 11 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 135 | blk.11.attn_k.weight | Block 11 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 136 | blk.11.attn_k_norm.weight | Block 11 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 137 | blk.11.attn_norm.weight | Block 11 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 138 | blk.11.attn_output.weight | Block 11 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 139 | blk.11.attn_q.weight | Block 11 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 140 | blk.11.attn_q_norm.weight | Block 11 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 141 | blk.11.attn_v.weight | Block 11 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 142 | blk.11.ffn_down_exps.weight | Block 11 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 143 | blk.11.ffn_gate_exps.weight | Block 11 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 144 | blk.11.ffn_gate_inp.weight | Block 11 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 145 | blk.11.ffn_norm.weight | Block 11 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 146 | blk.11.ffn_up_exps.weight | Block 11 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.11: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 12 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 147 | blk.12.attn_k.weight | Block 12 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 148 | blk.12.attn_k_norm.weight | Block 12 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 149 | blk.12.attn_norm.weight | Block 12 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 150 | blk.12.attn_output.weight | Block 12 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 151 | blk.12.attn_q.weight | Block 12 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 152 | blk.12.attn_q_norm.weight | Block 12 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 153 | blk.12.attn_v.weight | Block 12 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 154 | blk.12.ffn_down_exps.weight | Block 12 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 155 | blk.12.ffn_gate_exps.weight | Block 12 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 156 | blk.12.ffn_gate_inp.weight | Block 12 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 157 | blk.12.ffn_norm.weight | Block 12 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 158 | blk.12.ffn_up_exps.weight | Block 12 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.12: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 13 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 159 | blk.13.attn_k.weight | Block 13 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 160 | blk.13.attn_k_norm.weight | Block 13 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 161 | blk.13.attn_norm.weight | Block 13 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 162 | blk.13.attn_output.weight | Block 13 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 163 | blk.13.attn_q.weight | Block 13 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 164 | blk.13.attn_q_norm.weight | Block 13 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 165 | blk.13.attn_v.weight | Block 13 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 166 | blk.13.ffn_down_exps.weight | Block 13 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 167 | blk.13.ffn_gate_exps.weight | Block 13 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 168 | blk.13.ffn_gate_inp.weight | Block 13 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 169 | blk.13.ffn_norm.weight | Block 13 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 170 | blk.13.ffn_up_exps.weight | Block 13 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.13: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 14 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 171 | blk.14.attn_k.weight | Block 14 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 172 | blk.14.attn_k_norm.weight | Block 14 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 173 | blk.14.attn_norm.weight | Block 14 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 174 | blk.14.attn_output.weight | Block 14 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 175 | blk.14.attn_q.weight | Block 14 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 176 | blk.14.attn_q_norm.weight | Block 14 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 177 | blk.14.attn_v.weight | Block 14 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 178 | blk.14.ffn_down_exps.weight | Block 14 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 179 | blk.14.ffn_gate_exps.weight | Block 14 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 180 | blk.14.ffn_gate_inp.weight | Block 14 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 181 | blk.14.ffn_norm.weight | Block 14 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 182 | blk.14.ffn_up_exps.weight | Block 14 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.14: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 15 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 183 | blk.15.attn_k.weight | Block 15 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 184 | blk.15.attn_k_norm.weight | Block 15 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 185 | blk.15.attn_norm.weight | Block 15 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 186 | blk.15.attn_output.weight | Block 15 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 187 | blk.15.attn_q.weight | Block 15 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 188 | blk.15.attn_q_norm.weight | Block 15 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 189 | blk.15.attn_v.weight | Block 15 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 190 | blk.15.ffn_down_exps.weight | Block 15 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 191 | blk.15.ffn_gate_exps.weight | Block 15 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 192 | blk.15.ffn_gate_inp.weight | Block 15 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 193 | blk.15.ffn_norm.weight | Block 15 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 194 | blk.15.ffn_up_exps.weight | Block 15 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.15: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 16 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 195 | blk.16.attn_k.weight | Block 16 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 196 | blk.16.attn_k_norm.weight | Block 16 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 197 | blk.16.attn_norm.weight | Block 16 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 198 | blk.16.attn_output.weight | Block 16 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 199 | blk.16.attn_q.weight | Block 16 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 200 | blk.16.attn_q_norm.weight | Block 16 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 201 | blk.16.attn_v.weight | Block 16 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 202 | blk.16.ffn_down_exps.weight | Block 16 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 203 | blk.16.ffn_gate_exps.weight | Block 16 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 204 | blk.16.ffn_gate_inp.weight | Block 16 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 205 | blk.16.ffn_norm.weight | Block 16 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 206 | blk.16.ffn_up_exps.weight | Block 16 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.16: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 17 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 207 | blk.17.attn_k.weight | Block 17 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 208 | blk.17.attn_k_norm.weight | Block 17 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 209 | blk.17.attn_norm.weight | Block 17 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 210 | blk.17.attn_output.weight | Block 17 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 211 | blk.17.attn_q.weight | Block 17 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 212 | blk.17.attn_q_norm.weight | Block 17 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 213 | blk.17.attn_v.weight | Block 17 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 214 | blk.17.ffn_down_exps.weight | Block 17 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 215 | blk.17.ffn_gate_exps.weight | Block 17 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 216 | blk.17.ffn_gate_inp.weight | Block 17 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 217 | blk.17.ffn_norm.weight | Block 17 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 218 | blk.17.ffn_up_exps.weight | Block 17 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.17: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 18 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 219 | blk.18.attn_k.weight | Block 18 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 220 | blk.18.attn_k_norm.weight | Block 18 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 221 | blk.18.attn_norm.weight | Block 18 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 222 | blk.18.attn_output.weight | Block 18 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 223 | blk.18.attn_q.weight | Block 18 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 224 | blk.18.attn_q_norm.weight | Block 18 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 225 | blk.18.attn_v.weight | Block 18 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 226 | blk.18.ffn_down_exps.weight | Block 18 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 227 | blk.18.ffn_gate_exps.weight | Block 18 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 228 | blk.18.ffn_gate_inp.weight | Block 18 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 229 | blk.18.ffn_norm.weight | Block 18 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 230 | blk.18.ffn_up_exps.weight | Block 18 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.18: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 19 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 231 | blk.19.attn_k.weight | Block 19 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 232 | blk.19.attn_k_norm.weight | Block 19 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 233 | blk.19.attn_norm.weight | Block 19 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 234 | blk.19.attn_output.weight | Block 19 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 235 | blk.19.attn_q.weight | Block 19 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 236 | blk.19.attn_q_norm.weight | Block 19 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 237 | blk.19.attn_v.weight | Block 19 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 238 | blk.19.ffn_down_exps.weight | Block 19 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 239 | blk.19.ffn_gate_exps.weight | Block 19 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 240 | blk.19.ffn_gate_inp.weight | Block 19 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 241 | blk.19.ffn_norm.weight | Block 19 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 242 | blk.19.ffn_up_exps.weight | Block 19 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.19: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 20 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 243 | blk.20.attn_k.weight | Block 20 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 244 | blk.20.attn_k_norm.weight | Block 20 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 245 | blk.20.attn_norm.weight | Block 20 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 246 | blk.20.attn_output.weight | Block 20 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 247 | blk.20.attn_q.weight | Block 20 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 248 | blk.20.attn_q_norm.weight | Block 20 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 249 | blk.20.attn_v.weight | Block 20 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 250 | blk.20.ffn_down_exps.weight | Block 20 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 251 | blk.20.ffn_gate_exps.weight | Block 20 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 252 | blk.20.ffn_gate_inp.weight | Block 20 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 253 | blk.20.ffn_norm.weight | Block 20 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 254 | blk.20.ffn_up_exps.weight | Block 20 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.20: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 21 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 255 | blk.21.attn_k.weight | Block 21 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 256 | blk.21.attn_k_norm.weight | Block 21 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 257 | blk.21.attn_norm.weight | Block 21 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 258 | blk.21.attn_output.weight | Block 21 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 259 | blk.21.attn_q.weight | Block 21 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 260 | blk.21.attn_q_norm.weight | Block 21 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 261 | blk.21.attn_v.weight | Block 21 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 262 | blk.21.ffn_down_exps.weight | Block 21 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 263 | blk.21.ffn_gate_exps.weight | Block 21 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 264 | blk.21.ffn_gate_inp.weight | Block 21 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 265 | blk.21.ffn_norm.weight | Block 21 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 266 | blk.21.ffn_up_exps.weight | Block 21 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.21: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 22 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 267 | blk.22.attn_k.weight | Block 22 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 268 | blk.22.attn_k_norm.weight | Block 22 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 269 | blk.22.attn_norm.weight | Block 22 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 270 | blk.22.attn_output.weight | Block 22 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 271 | blk.22.attn_q.weight | Block 22 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 272 | blk.22.attn_q_norm.weight | Block 22 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 273 | blk.22.attn_v.weight | Block 22 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 274 | blk.22.ffn_down_exps.weight | Block 22 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 275 | blk.22.ffn_gate_exps.weight | Block 22 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 276 | blk.22.ffn_gate_inp.weight | Block 22 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 277 | blk.22.ffn_norm.weight | Block 22 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 278 | blk.22.ffn_up_exps.weight | Block 22 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.22: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 23 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 279 | blk.23.attn_k.weight | Block 23 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
+| 280 | blk.23.attn_k_norm.weight | Block 23 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 281 | blk.23.attn_norm.weight | Block 23 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 282 | blk.23.attn_output.weight | Block 23 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 283 | blk.23.attn_q.weight | Block 23 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
+| 284 | blk.23.attn_q_norm.weight | Block 23 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 285 | blk.23.attn_v.weight | Block 23 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
+| 286 | blk.23.ffn_down_exps.weight | Block 23 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 287 | blk.23.ffn_gate_exps.weight | Block 23 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 288 | blk.23.ffn_gate_inp.weight | Block 23 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 289 | blk.23.ffn_norm.weight | Block 23 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 290 | blk.23.ffn_up_exps.weight | Block 23 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.23: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 24 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 291 | blk.24.attn_k.weight | Block 24 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 292 | blk.24.attn_k_norm.weight | Block 24 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 293 | blk.24.attn_norm.weight | Block 24 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 294 | blk.24.attn_output.weight | Block 24 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 295 | blk.24.attn_q.weight | Block 24 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 296 | blk.24.attn_q_norm.weight | Block 24 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 297 | blk.24.attn_v.weight | Block 24 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 298 | blk.24.ffn_down_exps.weight | Block 24 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 299 | blk.24.ffn_gate_exps.weight | Block 24 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+| 300 | blk.24.ffn_gate_inp.weight | Block 24 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 301 | blk.24.ffn_norm.weight | Block 24 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 302 | blk.24.ffn_up_exps.weight | Block 24 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
+
+- Total elements in blk.24: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 25 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 303 | blk.25.attn_k.weight | Block 25 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 304 | blk.25.attn_k_norm.weight | Block 25 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 305 | blk.25.attn_norm.weight | Block 25 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 306 | blk.25.attn_output.weight | Block 25 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 307 | blk.25.attn_q.weight | Block 25 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 308 | blk.25.attn_q_norm.weight | Block 25 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 309 | blk.25.attn_v.weight | Block 25 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 310 | blk.25.ffn_down_exps.weight | Block 25 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 311 | blk.25.ffn_gate_exps.weight | Block 25 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 312 | blk.25.ffn_gate_inp.weight | Block 25 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 313 | blk.25.ffn_norm.weight | Block 25 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 314 | blk.25.ffn_up_exps.weight | Block 25 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.25: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 26 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 315 | blk.26.attn_k.weight | Block 26 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 316 | blk.26.attn_k_norm.weight | Block 26 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 317 | blk.26.attn_norm.weight | Block 26 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 318 | blk.26.attn_output.weight | Block 26 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 319 | blk.26.attn_q.weight | Block 26 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 320 | blk.26.attn_q_norm.weight | Block 26 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 321 | blk.26.attn_v.weight | Block 26 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 322 | blk.26.ffn_down_exps.weight | Block 26 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 323 | blk.26.ffn_gate_exps.weight | Block 26 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 324 | blk.26.ffn_gate_inp.weight | Block 26 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 325 | blk.26.ffn_norm.weight | Block 26 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 326 | blk.26.ffn_up_exps.weight | Block 26 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.26: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 27 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 327 | blk.27.attn_k.weight | Block 27 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 328 | blk.27.attn_k_norm.weight | Block 27 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 329 | blk.27.attn_norm.weight | Block 27 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 330 | blk.27.attn_output.weight | Block 27 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 331 | blk.27.attn_q.weight | Block 27 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 332 | blk.27.attn_q_norm.weight | Block 27 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 333 | blk.27.attn_v.weight | Block 27 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 334 | blk.27.ffn_down_exps.weight | Block 27 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 335 | blk.27.ffn_gate_exps.weight | Block 27 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 336 | blk.27.ffn_gate_inp.weight | Block 27 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 337 | blk.27.ffn_norm.weight | Block 27 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 338 | blk.27.ffn_up_exps.weight | Block 27 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.27: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 28 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 339 | blk.28.attn_k.weight | Block 28 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 340 | blk.28.attn_k_norm.weight | Block 28 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 341 | blk.28.attn_norm.weight | Block 28 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 342 | blk.28.attn_output.weight | Block 28 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 343 | blk.28.attn_q.weight | Block 28 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 344 | blk.28.attn_q_norm.weight | Block 28 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 345 | blk.28.attn_v.weight | Block 28 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 346 | blk.28.ffn_down_exps.weight | Block 28 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 347 | blk.28.ffn_gate_exps.weight | Block 28 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 348 | blk.28.ffn_gate_inp.weight | Block 28 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 349 | blk.28.ffn_norm.weight | Block 28 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 350 | blk.28.ffn_up_exps.weight | Block 28 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.28: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 29 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 351 | blk.29.attn_k.weight | Block 29 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 352 | blk.29.attn_k_norm.weight | Block 29 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 353 | blk.29.attn_norm.weight | Block 29 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 354 | blk.29.attn_output.weight | Block 29 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 355 | blk.29.attn_q.weight | Block 29 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 356 | blk.29.attn_q_norm.weight | Block 29 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 357 | blk.29.attn_v.weight | Block 29 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 358 | blk.29.ffn_down_exps.weight | Block 29 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 359 | blk.29.ffn_gate_exps.weight | Block 29 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 360 | blk.29.ffn_gate_inp.weight | Block 29 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 361 | blk.29.ffn_norm.weight | Block 29 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 362 | blk.29.ffn_up_exps.weight | Block 29 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.29: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 30 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 363 | blk.30.attn_k.weight | Block 30 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 364 | blk.30.attn_k_norm.weight | Block 30 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 365 | blk.30.attn_norm.weight | Block 30 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 366 | blk.30.attn_output.weight | Block 30 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 367 | blk.30.attn_q.weight | Block 30 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 368 | blk.30.attn_q_norm.weight | Block 30 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 369 | blk.30.attn_v.weight | Block 30 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 370 | blk.30.ffn_down_exps.weight | Block 30 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 371 | blk.30.ffn_gate_exps.weight | Block 30 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 372 | blk.30.ffn_gate_inp.weight | Block 30 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 373 | blk.30.ffn_norm.weight | Block 30 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 374 | blk.30.ffn_up_exps.weight | Block 30 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.30: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 31 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 375 | blk.31.attn_k.weight | Block 31 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 376 | blk.31.attn_k_norm.weight | Block 31 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 377 | blk.31.attn_norm.weight | Block 31 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 378 | blk.31.attn_output.weight | Block 31 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 379 | blk.31.attn_q.weight | Block 31 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 380 | blk.31.attn_q_norm.weight | Block 31 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 381 | blk.31.attn_v.weight | Block 31 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 382 | blk.31.ffn_down_exps.weight | Block 31 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 383 | blk.31.ffn_gate_exps.weight | Block 31 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 384 | blk.31.ffn_gate_inp.weight | Block 31 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 385 | blk.31.ffn_norm.weight | Block 31 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 386 | blk.31.ffn_up_exps.weight | Block 31 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.31: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 32 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 387 | blk.32.attn_k.weight | Block 32 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 388 | blk.32.attn_k_norm.weight | Block 32 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 389 | blk.32.attn_norm.weight | Block 32 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 390 | blk.32.attn_output.weight | Block 32 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 391 | blk.32.attn_q.weight | Block 32 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 392 | blk.32.attn_q_norm.weight | Block 32 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 393 | blk.32.attn_v.weight | Block 32 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 394 | blk.32.ffn_down_exps.weight | Block 32 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 395 | blk.32.ffn_gate_exps.weight | Block 32 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 396 | blk.32.ffn_gate_inp.weight | Block 32 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 397 | blk.32.ffn_norm.weight | Block 32 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 398 | blk.32.ffn_up_exps.weight | Block 32 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.32: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 33 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 399 | blk.33.attn_k.weight | Block 33 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 400 | blk.33.attn_k_norm.weight | Block 33 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 401 | blk.33.attn_norm.weight | Block 33 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 402 | blk.33.attn_output.weight | Block 33 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 403 | blk.33.attn_q.weight | Block 33 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 404 | blk.33.attn_q_norm.weight | Block 33 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 405 | blk.33.attn_v.weight | Block 33 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 406 | blk.33.ffn_down_exps.weight | Block 33 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 407 | blk.33.ffn_gate_exps.weight | Block 33 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 408 | blk.33.ffn_gate_inp.weight | Block 33 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 409 | blk.33.ffn_norm.weight | Block 33 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 410 | blk.33.ffn_up_exps.weight | Block 33 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.33: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 34 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 411 | blk.34.attn_k.weight | Block 34 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 412 | blk.34.attn_k_norm.weight | Block 34 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 413 | blk.34.attn_norm.weight | Block 34 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 414 | blk.34.attn_output.weight | Block 34 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 415 | blk.34.attn_q.weight | Block 34 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 416 | blk.34.attn_q_norm.weight | Block 34 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 417 | blk.34.attn_v.weight | Block 34 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 418 | blk.34.ffn_down_exps.weight | Block 34 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 419 | blk.34.ffn_gate_exps.weight | Block 34 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 420 | blk.34.ffn_gate_inp.weight | Block 34 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 421 | blk.34.ffn_norm.weight | Block 34 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 422 | blk.34.ffn_up_exps.weight | Block 34 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.34: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 35 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 423 | blk.35.attn_k.weight | Block 35 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 424 | blk.35.attn_k_norm.weight | Block 35 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 425 | blk.35.attn_norm.weight | Block 35 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 426 | blk.35.attn_output.weight | Block 35 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 427 | blk.35.attn_q.weight | Block 35 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 428 | blk.35.attn_q_norm.weight | Block 35 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 429 | blk.35.attn_v.weight | Block 35 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 430 | blk.35.ffn_down_exps.weight | Block 35 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 431 | blk.35.ffn_gate_exps.weight | Block 35 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 432 | blk.35.ffn_gate_inp.weight | Block 35 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 433 | blk.35.ffn_norm.weight | Block 35 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 434 | blk.35.ffn_up_exps.weight | Block 35 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.35: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 36 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 435 | blk.36.attn_k.weight | Block 36 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 436 | blk.36.attn_k_norm.weight | Block 36 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 437 | blk.36.attn_norm.weight | Block 36 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 438 | blk.36.attn_output.weight | Block 36 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 439 | blk.36.attn_q.weight | Block 36 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 440 | blk.36.attn_q_norm.weight | Block 36 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 441 | blk.36.attn_v.weight | Block 36 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 442 | blk.36.ffn_down_exps.weight | Block 36 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 443 | blk.36.ffn_gate_exps.weight | Block 36 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 444 | blk.36.ffn_gate_inp.weight | Block 36 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 445 | blk.36.ffn_norm.weight | Block 36 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 446 | blk.36.ffn_up_exps.weight | Block 36 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.36: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 37 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 447 | blk.37.attn_k.weight | Block 37 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 448 | blk.37.attn_k_norm.weight | Block 37 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 449 | blk.37.attn_norm.weight | Block 37 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 450 | blk.37.attn_output.weight | Block 37 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 451 | blk.37.attn_q.weight | Block 37 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 452 | blk.37.attn_q_norm.weight | Block 37 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 453 | blk.37.attn_v.weight | Block 37 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 454 | blk.37.ffn_down_exps.weight | Block 37 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 455 | blk.37.ffn_gate_exps.weight | Block 37 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 456 | blk.37.ffn_gate_inp.weight | Block 37 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 457 | blk.37.ffn_norm.weight | Block 37 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 458 | blk.37.ffn_up_exps.weight | Block 37 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.37: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 38 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 459 | blk.38.attn_k.weight | Block 38 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 460 | blk.38.attn_k_norm.weight | Block 38 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 461 | blk.38.attn_norm.weight | Block 38 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 462 | blk.38.attn_output.weight | Block 38 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 463 | blk.38.attn_q.weight | Block 38 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 464 | blk.38.attn_q_norm.weight | Block 38 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 465 | blk.38.attn_v.weight | Block 38 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 466 | blk.38.ffn_down_exps.weight | Block 38 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 467 | blk.38.ffn_gate_exps.weight | Block 38 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 468 | blk.38.ffn_gate_inp.weight | Block 38 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 469 | blk.38.ffn_norm.weight | Block 38 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 470 | blk.38.ffn_up_exps.weight | Block 38 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.38: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 39 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 471 | blk.39.attn_k.weight | Block 39 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 472 | blk.39.attn_k_norm.weight | Block 39 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 473 | blk.39.attn_norm.weight | Block 39 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 474 | blk.39.attn_output.weight | Block 39 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 475 | blk.39.attn_q.weight | Block 39 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 476 | blk.39.attn_q_norm.weight | Block 39 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 477 | blk.39.attn_v.weight | Block 39 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 478 | blk.39.ffn_down_exps.weight | Block 39 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 479 | blk.39.ffn_gate_exps.weight | Block 39 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 480 | blk.39.ffn_gate_inp.weight | Block 39 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 481 | blk.39.ffn_norm.weight | Block 39 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 482 | blk.39.ffn_up_exps.weight | Block 39 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.39: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 40 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 483 | blk.40.attn_k.weight | Block 40 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 484 | blk.40.attn_k_norm.weight | Block 40 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 485 | blk.40.attn_norm.weight | Block 40 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 486 | blk.40.attn_output.weight | Block 40 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 487 | blk.40.attn_q.weight | Block 40 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 488 | blk.40.attn_q_norm.weight | Block 40 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 489 | blk.40.attn_v.weight | Block 40 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 490 | blk.40.ffn_down_exps.weight | Block 40 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 491 | blk.40.ffn_gate_exps.weight | Block 40 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 492 | blk.40.ffn_gate_inp.weight | Block 40 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 493 | blk.40.ffn_norm.weight | Block 40 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 494 | blk.40.ffn_up_exps.weight | Block 40 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.40: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 41 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 495 | blk.41.attn_k.weight | Block 41 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 496 | blk.41.attn_k_norm.weight | Block 41 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 497 | blk.41.attn_norm.weight | Block 41 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 498 | blk.41.attn_output.weight | Block 41 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 499 | blk.41.attn_q.weight | Block 41 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 500 | blk.41.attn_q_norm.weight | Block 41 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 501 | blk.41.attn_v.weight | Block 41 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 502 | blk.41.ffn_down_exps.weight | Block 41 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 503 | blk.41.ffn_gate_exps.weight | Block 41 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 504 | blk.41.ffn_gate_inp.weight | Block 41 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 505 | blk.41.ffn_norm.weight | Block 41 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 506 | blk.41.ffn_up_exps.weight | Block 41 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.41: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 42 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 507 | blk.42.attn_k.weight | Block 42 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 508 | blk.42.attn_k_norm.weight | Block 42 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 509 | blk.42.attn_norm.weight | Block 42 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 510 | blk.42.attn_output.weight | Block 42 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 511 | blk.42.attn_q.weight | Block 42 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 512 | blk.42.attn_q_norm.weight | Block 42 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 513 | blk.42.attn_v.weight | Block 42 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 514 | blk.42.ffn_down_exps.weight | Block 42 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 515 | blk.42.ffn_gate_exps.weight | Block 42 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 516 | blk.42.ffn_gate_inp.weight | Block 42 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 517 | blk.42.ffn_norm.weight | Block 42 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 518 | blk.42.ffn_up_exps.weight | Block 42 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.42: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 43 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 519 | blk.43.attn_k.weight | Block 43 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 520 | blk.43.attn_k_norm.weight | Block 43 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 521 | blk.43.attn_norm.weight | Block 43 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 522 | blk.43.attn_output.weight | Block 43 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 523 | blk.43.attn_q.weight | Block 43 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 524 | blk.43.attn_q_norm.weight | Block 43 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 525 | blk.43.attn_v.weight | Block 43 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 526 | blk.43.ffn_down_exps.weight | Block 43 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 527 | blk.43.ffn_gate_exps.weight | Block 43 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 528 | blk.43.ffn_gate_inp.weight | Block 43 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 529 | blk.43.ffn_norm.weight | Block 43 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 530 | blk.43.ffn_up_exps.weight | Block 43 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.43: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 44 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 531 | blk.44.attn_k.weight | Block 44 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 532 | blk.44.attn_k_norm.weight | Block 44 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 533 | blk.44.attn_norm.weight | Block 44 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 534 | blk.44.attn_output.weight | Block 44 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 535 | blk.44.attn_q.weight | Block 44 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 536 | blk.44.attn_q_norm.weight | Block 44 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 537 | blk.44.attn_v.weight | Block 44 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 538 | blk.44.ffn_down_exps.weight | Block 44 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 539 | blk.44.ffn_gate_exps.weight | Block 44 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 540 | blk.44.ffn_gate_inp.weight | Block 44 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 541 | blk.44.ffn_norm.weight | Block 44 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 542 | blk.44.ffn_up_exps.weight | Block 44 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.44: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+### Block 45 Tensor Group : ~623M Elements
+
+| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
+|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
+| 543 | blk.45.attn_k.weight | Block 45 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 544 | blk.45.attn_k_norm.weight | Block 45 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 545 | blk.45.attn_norm.weight | Block 45 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 546 | blk.45.attn_output.weight | Block 45 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
+| 547 | blk.45.attn_q.weight | Block 45 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
+| 548 | blk.45.attn_q_norm.weight | Block 45 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
+| 549 | blk.45.attn_v.weight | Block 45 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
+| 550 | blk.45.ffn_down_exps.weight | Block 45 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
+| 551 | blk.45.ffn_gate_exps.weight | Block 45 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+| 552 | blk.45.ffn_gate_inp.weight | Block 45 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
+| 553 | blk.45.ffn_norm.weight | Block 45 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
+| 554 | blk.45.ffn_up_exps.weight | Block 45 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
+
+- Total elements in blk.45: (~623M) 623120640
+- Percentage of total elements: 2.13%
+
+
+