Qwen3-30B-A3B-Q8_0.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 %}{{- '< |
| 38 | UINT32 | 1 | general.quantization_version | 2 |
| 39 | UINT32 | 1 | general.file_type | 7 |
| 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-Q8_0.gguf - GGUF Internal File Dump
- Key Value Metadata Store
- Tensors Overview ~29B Elements
- Tensor Data Offset
- Base Tensor Group : ~622M Elements
- Block 0 Tensor Group : ~623M Elements
- Block 1 Tensor Group : ~623M Elements
- Block 2 Tensor Group : ~623M Elements
- Block 3 Tensor Group : ~623M Elements
- Block 4 Tensor Group : ~623M Elements
- Block 5 Tensor Group : ~623M Elements
- Block 6 Tensor Group : ~623M Elements
- Block 7 Tensor Group : ~623M Elements
- Block 8 Tensor Group : ~623M Elements
- Block 9 Tensor Group : ~623M Elements
- Block 10 Tensor Group : ~623M Elements
- Block 11 Tensor Group : ~623M Elements
- Block 12 Tensor Group : ~623M Elements
- Block 13 Tensor Group : ~623M Elements
- Block 14 Tensor Group : ~623M Elements
- Block 15 Tensor Group : ~623M Elements
- Block 16 Tensor Group : ~623M Elements
- Block 17 Tensor Group : ~623M Elements
- Block 18 Tensor Group : ~623M Elements
- Block 19 Tensor Group : ~623M Elements
- Block 20 Tensor Group : ~623M Elements
- Block 21 Tensor Group : ~623M Elements
- Block 22 Tensor Group : ~623M Elements
- Block 23 Tensor Group : ~623M Elements
- Block 24 Tensor Group : ~623M Elements
- Block 25 Tensor Group : ~623M Elements
- Block 26 Tensor Group : ~623M Elements
- Block 27 Tensor Group : ~623M Elements
- Block 28 Tensor Group : ~623M Elements
- Block 29 Tensor Group : ~623M Elements
- Block 30 Tensor Group : ~623M Elements
- Block 31 Tensor Group : ~623M Elements
- Block 32 Tensor Group : ~623M Elements
- Block 33 Tensor Group : ~623M Elements
- Block 34 Tensor Group : ~623M Elements
- Block 35 Tensor Group : ~623M Elements
- Block 36 Tensor Group : ~623M Elements
- Block 37 Tensor Group : ~623M Elements
- Block 38 Tensor Group : ~623M Elements
- Block 39 Tensor Group : ~623M Elements
- Block 40 Tensor Group : ~623M Elements
- Block 41 Tensor Group : ~623M Elements
- Block 42 Tensor Group : ~623M Elements
- Block 43 Tensor Group : ~623M Elements
- Block 44 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 | 0x13b4c000 |
| 1 | output_norm.weight | 0x140fd2e0 | 0x2000 |
| 2 | token_embd.weight | 0x140ff2e0 | 0x7f82800 |
| 3 | blk.0.attn_k.weight | 0x1c081ae0 | 0xd2000 |
| 4 | blk.0.attn_k_norm.weight | 0x1c153ae0 | 0x200 |
| 5 | blk.0.attn_norm.weight | 0x1c153ce0 | 0x2000 |
| 6 | blk.0.attn_output.weight | 0x1c155ce0 | 0x880000 |
| 7 | blk.0.attn_q.weight | 0x1c9d5ce0 | 0x690000 |
| 8 | blk.0.attn_q_norm.weight | 0x1d065ce0 | 0x200 |
| 9 | blk.0.attn_v.weight | 0x1d065ee0 | 0x200000 |
| 10 | blk.0.ffn_down_exps.weight | 0x1d265ee0 | 0xcc00000 |
| 11 | blk.0.ffn_gate_exps.weight | 0x29e65ee0 | 0x9d80000 |
| 12 | blk.0.ffn_gate_inp.weight | 0x33be5ee0 | 0x100000 |
| 13 | blk.0.ffn_norm.weight | 0x33ce5ee0 | 0x2000 |
| 14 | blk.0.ffn_up_exps.weight | 0x33ce7ee0 | 0x9d80000 |
| 15 | blk.1.attn_k.weight | 0x3da67ee0 | 0xd2000 |
| 16 | blk.1.attn_k_norm.weight | 0x3db39ee0 | 0x200 |
| 17 | blk.1.attn_norm.weight | 0x3db3a0e0 | 0x2000 |
| 18 | blk.1.attn_output.weight | 0x3db3c0e0 | 0x880000 |
| 19 | blk.1.attn_q.weight | 0x3e3bc0e0 | 0x690000 |
| 20 | blk.1.attn_q_norm.weight | 0x3ea4c0e0 | 0x200 |
| 21 | blk.1.attn_v.weight | 0x3ea4c2e0 | 0x200000 |
| 22 | blk.1.ffn_down_exps.weight | 0x3ec4c2e0 | 0xcc00000 |
| 23 | blk.1.ffn_gate_exps.weight | 0x4b84c2e0 | 0x9d80000 |
| 24 | blk.1.ffn_gate_inp.weight | 0x555cc2e0 | 0x100000 |
| 25 | blk.1.ffn_norm.weight | 0x556cc2e0 | 0x2000 |
| 26 | blk.1.ffn_up_exps.weight | 0x556ce2e0 | 0x9d80000 |
| 27 | blk.2.attn_k.weight | 0x5f44e2e0 | 0xd2000 |
| 28 | blk.2.attn_k_norm.weight | 0x5f5202e0 | 0x200 |
| 29 | blk.2.attn_norm.weight | 0x5f5204e0 | 0x2000 |
| 30 | blk.2.attn_output.weight | 0x5f5224e0 | 0x880000 |
| 31 | blk.2.attn_q.weight | 0x5fda24e0 | 0x690000 |
| 32 | blk.2.attn_q_norm.weight | 0x604324e0 | 0x200 |
| 33 | blk.2.attn_v.weight | 0x604326e0 | 0x200000 |
| 34 | blk.2.ffn_down_exps.weight | 0x606326e0 | 0xcc00000 |
| 35 | blk.2.ffn_gate_exps.weight | 0x6d2326e0 | 0x9d80000 |
| 36 | blk.2.ffn_gate_inp.weight | 0x76fb26e0 | 0x100000 |
| 37 | blk.2.ffn_norm.weight | 0x770b26e0 | 0x2000 |
| 38 | blk.2.ffn_up_exps.weight | 0x770b46e0 | 0x9d80000 |
| 39 | blk.3.attn_k.weight | 0x80e346e0 | 0xd2000 |
| 40 | blk.3.attn_k_norm.weight | 0x80f066e0 | 0x200 |
| 41 | blk.3.attn_norm.weight | 0x80f068e0 | 0x2000 |
| 42 | blk.3.attn_output.weight | 0x80f088e0 | 0x880000 |
| 43 | blk.3.attn_q.weight | 0x817888e0 | 0x690000 |
| 44 | blk.3.attn_q_norm.weight | 0x81e188e0 | 0x200 |
| 45 | blk.3.attn_v.weight | 0x81e18ae0 | 0x200000 |
| 46 | blk.3.ffn_down_exps.weight | 0x82018ae0 | 0xcc00000 |
| 47 | blk.3.ffn_gate_exps.weight | 0x8ec18ae0 | 0x9d80000 |
| 48 | blk.3.ffn_gate_inp.weight | 0x98998ae0 | 0x100000 |
| 49 | blk.3.ffn_norm.weight | 0x98a98ae0 | 0x2000 |
| 50 | blk.3.ffn_up_exps.weight | 0x98a9aae0 | 0x9d80000 |
| 51 | blk.4.attn_k.weight | 0xa281aae0 | 0xd2000 |
| 52 | blk.4.attn_k_norm.weight | 0xa28ecae0 | 0x200 |
| 53 | blk.4.attn_norm.weight | 0xa28ecce0 | 0x2000 |
| 54 | blk.4.attn_output.weight | 0xa28eece0 | 0x880000 |
| 55 | blk.4.attn_q.weight | 0xa316ece0 | 0x690000 |
| 56 | blk.4.attn_q_norm.weight | 0xa37fece0 | 0x200 |
| 57 | blk.4.attn_v.weight | 0xa37feee0 | 0x200000 |
| 58 | blk.4.ffn_down_exps.weight | 0xa39feee0 | 0xcc00000 |
| 59 | blk.4.ffn_gate_exps.weight | 0xb05feee0 | 0x9d80000 |
| 60 | blk.4.ffn_gate_inp.weight | 0xba37eee0 | 0x100000 |
| 61 | blk.4.ffn_norm.weight | 0xba47eee0 | 0x2000 |
| 62 | blk.4.ffn_up_exps.weight | 0xba480ee0 | 0x9d80000 |
| 63 | blk.5.attn_k.weight | 0xc4200ee0 | 0xd2000 |
| 64 | blk.5.attn_k_norm.weight | 0xc42d2ee0 | 0x200 |
| 65 | blk.5.attn_norm.weight | 0xc42d30e0 | 0x2000 |
| 66 | blk.5.attn_output.weight | 0xc42d50e0 | 0x880000 |
| 67 | blk.5.attn_q.weight | 0xc4b550e0 | 0x690000 |
| 68 | blk.5.attn_q_norm.weight | 0xc51e50e0 | 0x200 |
| 69 | blk.5.attn_v.weight | 0xc51e52e0 | 0x200000 |
| 70 | blk.5.ffn_down_exps.weight | 0xc53e52e0 | 0xcc00000 |
| 71 | blk.5.ffn_gate_exps.weight | 0xd1fe52e0 | 0x9d80000 |
| 72 | blk.5.ffn_gate_inp.weight | 0xdbd652e0 | 0x100000 |
| 73 | blk.5.ffn_norm.weight | 0xdbe652e0 | 0x2000 |
| 74 | blk.5.ffn_up_exps.weight | 0xdbe672e0 | 0x9d80000 |
| 75 | blk.6.attn_k.weight | 0xe5be72e0 | 0xd2000 |
| 76 | blk.6.attn_k_norm.weight | 0xe5cb92e0 | 0x200 |
| 77 | blk.6.attn_norm.weight | 0xe5cb94e0 | 0x2000 |
| 78 | blk.6.attn_output.weight | 0xe5cbb4e0 | 0x880000 |
| 79 | blk.6.attn_q.weight | 0xe653b4e0 | 0x690000 |
| 80 | blk.6.attn_q_norm.weight | 0xe6bcb4e0 | 0x200 |
| 81 | blk.6.attn_v.weight | 0xe6bcb6e0 | 0x200000 |
| 82 | blk.6.ffn_down_exps.weight | 0xe6dcb6e0 | 0xcc00000 |
| 83 | blk.6.ffn_gate_exps.weight | 0xf39cb6e0 | 0x9d80000 |
| 84 | blk.6.ffn_gate_inp.weight | 0xfd74b6e0 | 0x100000 |
| 85 | blk.6.ffn_norm.weight | 0xfd84b6e0 | 0x2000 |
| 86 | blk.6.ffn_up_exps.weight | 0xfd84d6e0 | 0x9d80000 |
| 87 | blk.7.attn_k.weight | 0x1075cd6e0 | 0xd2000 |
| 88 | blk.7.attn_k_norm.weight | 0x10769f6e0 | 0x200 |
| 89 | blk.7.attn_norm.weight | 0x10769f8e0 | 0x2000 |
| 90 | blk.7.attn_output.weight | 0x1076a18e0 | 0x880000 |
| 91 | blk.7.attn_q.weight | 0x107f218e0 | 0x690000 |
| 92 | blk.7.attn_q_norm.weight | 0x1085b18e0 | 0x200 |
| 93 | blk.7.attn_v.weight | 0x1085b1ae0 | 0x200000 |
| 94 | blk.7.ffn_down_exps.weight | 0x1087b1ae0 | 0xcc00000 |
| 95 | blk.7.ffn_gate_exps.weight | 0x1153b1ae0 | 0x9d80000 |
| 96 | blk.7.ffn_gate_inp.weight | 0x11f131ae0 | 0x100000 |
| 97 | blk.7.ffn_norm.weight | 0x11f231ae0 | 0x2000 |
| 98 | blk.7.ffn_up_exps.weight | 0x11f233ae0 | 0x9d80000 |
| 99 | blk.8.attn_k.weight | 0x128fb3ae0 | 0xd2000 |
| 100 | blk.8.attn_k_norm.weight | 0x129085ae0 | 0x200 |
| 101 | blk.8.attn_norm.weight | 0x129085ce0 | 0x2000 |
| 102 | blk.8.attn_output.weight | 0x129087ce0 | 0x880000 |
| 103 | blk.8.attn_q.weight | 0x129907ce0 | 0x690000 |
| 104 | blk.8.attn_q_norm.weight | 0x129f97ce0 | 0x200 |
| 105 | blk.8.attn_v.weight | 0x129f97ee0 | 0x200000 |
| 106 | blk.8.ffn_down_exps.weight | 0x12a197ee0 | 0xcc00000 |
| 107 | blk.8.ffn_gate_exps.weight | 0x136d97ee0 | 0x9d80000 |
| 108 | blk.8.ffn_gate_inp.weight | 0x140b17ee0 | 0x100000 |
| 109 | blk.8.ffn_norm.weight | 0x140c17ee0 | 0x2000 |
| 110 | blk.8.ffn_up_exps.weight | 0x140c19ee0 | 0x9d80000 |
| 111 | blk.9.attn_k.weight | 0x14a999ee0 | 0xd2000 |
| 112 | blk.9.attn_k_norm.weight | 0x14aa6bee0 | 0x200 |
| 113 | blk.9.attn_norm.weight | 0x14aa6c0e0 | 0x2000 |
| 114 | blk.9.attn_output.weight | 0x14aa6e0e0 | 0x880000 |
| 115 | blk.9.attn_q.weight | 0x14b2ee0e0 | 0x690000 |
| 116 | blk.9.attn_q_norm.weight | 0x14b97e0e0 | 0x200 |
| 117 | blk.9.attn_v.weight | 0x14b97e2e0 | 0x200000 |
| 118 | blk.9.ffn_down_exps.weight | 0x14bb7e2e0 | 0xcc00000 |
| 119 | blk.9.ffn_gate_exps.weight | 0x15877e2e0 | 0x9d80000 |
| 120 | blk.9.ffn_gate_inp.weight | 0x1624fe2e0 | 0x100000 |
| 121 | blk.9.ffn_norm.weight | 0x1625fe2e0 | 0x2000 |
| 122 | blk.9.ffn_up_exps.weight | 0x1626002e0 | 0x9d80000 |
| 123 | blk.10.attn_k.weight | 0x16c3802e0 | 0xd2000 |
| 124 | blk.10.attn_k_norm.weight | 0x16c4522e0 | 0x200 |
| 125 | blk.10.attn_norm.weight | 0x16c4524e0 | 0x2000 |
| 126 | blk.10.attn_output.weight | 0x16c4544e0 | 0x880000 |
| 127 | blk.10.attn_q.weight | 0x16ccd44e0 | 0x690000 |
| 128 | blk.10.attn_q_norm.weight | 0x16d3644e0 | 0x200 |
| 129 | blk.10.attn_v.weight | 0x16d3646e0 | 0x200000 |
| 130 | blk.10.ffn_down_exps.weight | 0x16d5646e0 | 0xcc00000 |
| 131 | blk.10.ffn_gate_exps.weight | 0x17a1646e0 | 0x9d80000 |
| 132 | blk.10.ffn_gate_inp.weight | 0x183ee46e0 | 0x100000 |
| 133 | blk.10.ffn_norm.weight | 0x183fe46e0 | 0x2000 |
| 134 | blk.10.ffn_up_exps.weight | 0x183fe66e0 | 0x9d80000 |
| 135 | blk.11.attn_k.weight | 0x18dd666e0 | 0xd2000 |
| 136 | blk.11.attn_k_norm.weight | 0x18de386e0 | 0x200 |
| 137 | blk.11.attn_norm.weight | 0x18de388e0 | 0x2000 |
| 138 | blk.11.attn_output.weight | 0x18de3a8e0 | 0x880000 |
| 139 | blk.11.attn_q.weight | 0x18e6ba8e0 | 0x690000 |
| 140 | blk.11.attn_q_norm.weight | 0x18ed4a8e0 | 0x200 |
| 141 | blk.11.attn_v.weight | 0x18ed4aae0 | 0x200000 |
| 142 | blk.11.ffn_down_exps.weight | 0x18ef4aae0 | 0xcc00000 |
| 143 | blk.11.ffn_gate_exps.weight | 0x19bb4aae0 | 0x9d80000 |
| 144 | blk.11.ffn_gate_inp.weight | 0x1a58caae0 | 0x100000 |
| 145 | blk.11.ffn_norm.weight | 0x1a59caae0 | 0x2000 |
| 146 | blk.11.ffn_up_exps.weight | 0x1a59ccae0 | 0x9d80000 |
| 147 | blk.12.attn_k.weight | 0x1af74cae0 | 0xd2000 |
| 148 | blk.12.attn_k_norm.weight | 0x1af81eae0 | 0x200 |
| 149 | blk.12.attn_norm.weight | 0x1af81ece0 | 0x2000 |
| 150 | blk.12.attn_output.weight | 0x1af820ce0 | 0x880000 |
| 151 | blk.12.attn_q.weight | 0x1b00a0ce0 | 0x690000 |
| 152 | blk.12.attn_q_norm.weight | 0x1b0730ce0 | 0x200 |
| 153 | blk.12.attn_v.weight | 0x1b0730ee0 | 0x200000 |
| 154 | blk.12.ffn_down_exps.weight | 0x1b0930ee0 | 0xcc00000 |
| 155 | blk.12.ffn_gate_exps.weight | 0x1bd530ee0 | 0x9d80000 |
| 156 | blk.12.ffn_gate_inp.weight | 0x1c72b0ee0 | 0x100000 |
| 157 | blk.12.ffn_norm.weight | 0x1c73b0ee0 | 0x2000 |
| 158 | blk.12.ffn_up_exps.weight | 0x1c73b2ee0 | 0x9d80000 |
| 159 | blk.13.attn_k.weight | 0x1d1132ee0 | 0xd2000 |
| 160 | blk.13.attn_k_norm.weight | 0x1d1204ee0 | 0x200 |
| 161 | blk.13.attn_norm.weight | 0x1d12050e0 | 0x2000 |
| 162 | blk.13.attn_output.weight | 0x1d12070e0 | 0x880000 |
| 163 | blk.13.attn_q.weight | 0x1d1a870e0 | 0x690000 |
| 164 | blk.13.attn_q_norm.weight | 0x1d21170e0 | 0x200 |
| 165 | blk.13.attn_v.weight | 0x1d21172e0 | 0x200000 |
| 166 | blk.13.ffn_down_exps.weight | 0x1d23172e0 | 0xcc00000 |
| 167 | blk.13.ffn_gate_exps.weight | 0x1def172e0 | 0x9d80000 |
| 168 | blk.13.ffn_gate_inp.weight | 0x1e8c972e0 | 0x100000 |
| 169 | blk.13.ffn_norm.weight | 0x1e8d972e0 | 0x2000 |
| 170 | blk.13.ffn_up_exps.weight | 0x1e8d992e0 | 0x9d80000 |
| 171 | blk.14.attn_k.weight | 0x1f2b192e0 | 0xd2000 |
| 172 | blk.14.attn_k_norm.weight | 0x1f2beb2e0 | 0x200 |
| 173 | blk.14.attn_norm.weight | 0x1f2beb4e0 | 0x2000 |
| 174 | blk.14.attn_output.weight | 0x1f2bed4e0 | 0x880000 |
| 175 | blk.14.attn_q.weight | 0x1f346d4e0 | 0x690000 |
| 176 | blk.14.attn_q_norm.weight | 0x1f3afd4e0 | 0x200 |
| 177 | blk.14.attn_v.weight | 0x1f3afd6e0 | 0x200000 |
| 178 | blk.14.ffn_down_exps.weight | 0x1f3cfd6e0 | 0xcc00000 |
| 179 | blk.14.ffn_gate_exps.weight | 0x2008fd6e0 | 0x9d80000 |
| 180 | blk.14.ffn_gate_inp.weight | 0x20a67d6e0 | 0x100000 |
| 181 | blk.14.ffn_norm.weight | 0x20a77d6e0 | 0x2000 |
| 182 | blk.14.ffn_up_exps.weight | 0x20a77f6e0 | 0x9d80000 |
| 183 | blk.15.attn_k.weight | 0x2144ff6e0 | 0xd2000 |
| 184 | blk.15.attn_k_norm.weight | 0x2145d16e0 | 0x200 |
| 185 | blk.15.attn_norm.weight | 0x2145d18e0 | 0x2000 |
| 186 | blk.15.attn_output.weight | 0x2145d38e0 | 0x880000 |
| 187 | blk.15.attn_q.weight | 0x214e538e0 | 0x690000 |
| 188 | blk.15.attn_q_norm.weight | 0x2154e38e0 | 0x200 |
| 189 | blk.15.attn_v.weight | 0x2154e3ae0 | 0x200000 |
| 190 | blk.15.ffn_down_exps.weight | 0x2156e3ae0 | 0xcc00000 |
| 191 | blk.15.ffn_gate_exps.weight | 0x2222e3ae0 | 0x9d80000 |
| 192 | blk.15.ffn_gate_inp.weight | 0x22c063ae0 | 0x100000 |
| 193 | blk.15.ffn_norm.weight | 0x22c163ae0 | 0x2000 |
| 194 | blk.15.ffn_up_exps.weight | 0x22c165ae0 | 0x9d80000 |
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| 197 | blk.16.attn_norm.weight | 0x235fb7ce0 | 0x2000 |
| 198 | blk.16.attn_output.weight | 0x235fb9ce0 | 0x880000 |
| 199 | blk.16.attn_q.weight | 0x236839ce0 | 0x690000 |
| 200 | blk.16.attn_q_norm.weight | 0x236ec9ce0 | 0x200 |
| 201 | blk.16.attn_v.weight | 0x236ec9ee0 | 0x200000 |
| 202 | blk.16.ffn_down_exps.weight | 0x2370c9ee0 | 0xcc00000 |
| 203 | blk.16.ffn_gate_exps.weight | 0x243cc9ee0 | 0x9d80000 |
| 204 | blk.16.ffn_gate_inp.weight | 0x24da49ee0 | 0x100000 |
| 205 | blk.16.ffn_norm.weight | 0x24db49ee0 | 0x2000 |
| 206 | blk.16.ffn_up_exps.weight | 0x24db4bee0 | 0x9d80000 |
| 207 | blk.17.attn_k.weight | 0x2578cbee0 | 0xd2000 |
| 208 | blk.17.attn_k_norm.weight | 0x25799dee0 | 0x200 |
| 209 | blk.17.attn_norm.weight | 0x25799e0e0 | 0x2000 |
| 210 | blk.17.attn_output.weight | 0x2579a00e0 | 0x880000 |
| 211 | blk.17.attn_q.weight | 0x2582200e0 | 0x690000 |
| 212 | blk.17.attn_q_norm.weight | 0x2588b00e0 | 0x200 |
| 213 | blk.17.attn_v.weight | 0x2588b02e0 | 0x200000 |
| 214 | blk.17.ffn_down_exps.weight | 0x258ab02e0 | 0xcc00000 |
| 215 | blk.17.ffn_gate_exps.weight | 0x2656b02e0 | 0x9d80000 |
| 216 | blk.17.ffn_gate_inp.weight | 0x26f4302e0 | 0x100000 |
| 217 | blk.17.ffn_norm.weight | 0x26f5302e0 | 0x2000 |
| 218 | blk.17.ffn_up_exps.weight | 0x26f5322e0 | 0x9d80000 |
| 219 | blk.18.attn_k.weight | 0x2792b22e0 | 0xd2000 |
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| 221 | blk.18.attn_norm.weight | 0x2793844e0 | 0x2000 |
| 222 | blk.18.attn_output.weight | 0x2793864e0 | 0x880000 |
| 223 | blk.18.attn_q.weight | 0x279c064e0 | 0x690000 |
| 224 | blk.18.attn_q_norm.weight | 0x27a2964e0 | 0x200 |
| 225 | blk.18.attn_v.weight | 0x27a2966e0 | 0x200000 |
| 226 | blk.18.ffn_down_exps.weight | 0x27a4966e0 | 0xcc00000 |
| 227 | blk.18.ffn_gate_exps.weight | 0x2870966e0 | 0xcc00000 |
| 228 | blk.18.ffn_gate_inp.weight | 0x293c966e0 | 0x100000 |
| 229 | blk.18.ffn_norm.weight | 0x293d966e0 | 0x2000 |
| 230 | blk.18.ffn_up_exps.weight | 0x293d986e0 | 0xcc00000 |
| 231 | blk.19.attn_k.weight | 0x2a09986e0 | 0xd2000 |
| 232 | blk.19.attn_k_norm.weight | 0x2a0a6a6e0 | 0x200 |
| 233 | blk.19.attn_norm.weight | 0x2a0a6a8e0 | 0x2000 |
| 234 | blk.19.attn_output.weight | 0x2a0a6c8e0 | 0x880000 |
| 235 | blk.19.attn_q.weight | 0x2a12ec8e0 | 0x690000 |
| 236 | blk.19.attn_q_norm.weight | 0x2a197c8e0 | 0x200 |
| 237 | blk.19.attn_v.weight | 0x2a197cae0 | 0x200000 |
| 238 | blk.19.ffn_down_exps.weight | 0x2a1b7cae0 | 0xcc00000 |
| 239 | blk.19.ffn_gate_exps.weight | 0x2ae77cae0 | 0x9d80000 |
| 240 | blk.19.ffn_gate_inp.weight | 0x2b84fcae0 | 0x100000 |
| 241 | blk.19.ffn_norm.weight | 0x2b85fcae0 | 0x2000 |
| 242 | blk.19.ffn_up_exps.weight | 0x2b85feae0 | 0x9d80000 |
| 243 | blk.20.attn_k.weight | 0x2c237eae0 | 0xd2000 |
| 244 | blk.20.attn_k_norm.weight | 0x2c2450ae0 | 0x200 |
| 245 | blk.20.attn_norm.weight | 0x2c2450ce0 | 0x2000 |
| 246 | blk.20.attn_output.weight | 0x2c2452ce0 | 0x880000 |
| 247 | blk.20.attn_q.weight | 0x2c2cd2ce0 | 0x690000 |
| 248 | blk.20.attn_q_norm.weight | 0x2c3362ce0 | 0x200 |
| 249 | blk.20.attn_v.weight | 0x2c3362ee0 | 0x200000 |
| 250 | blk.20.ffn_down_exps.weight | 0x2c3562ee0 | 0xcc00000 |
| 251 | blk.20.ffn_gate_exps.weight | 0x2d0162ee0 | 0x9d80000 |
| 252 | blk.20.ffn_gate_inp.weight | 0x2d9ee2ee0 | 0x100000 |
| 253 | blk.20.ffn_norm.weight | 0x2d9fe2ee0 | 0x2000 |
| 254 | blk.20.ffn_up_exps.weight | 0x2d9fe4ee0 | 0x9d80000 |
| 255 | blk.21.attn_k.weight | 0x2e3d64ee0 | 0xd2000 |
| 256 | blk.21.attn_k_norm.weight | 0x2e3e36ee0 | 0x200 |
| 257 | blk.21.attn_norm.weight | 0x2e3e370e0 | 0x2000 |
| 258 | blk.21.attn_output.weight | 0x2e3e390e0 | 0x880000 |
| 259 | blk.21.attn_q.weight | 0x2e46b90e0 | 0x690000 |
| 260 | blk.21.attn_q_norm.weight | 0x2e4d490e0 | 0x200 |
| 261 | blk.21.attn_v.weight | 0x2e4d492e0 | 0x200000 |
| 262 | blk.21.ffn_down_exps.weight | 0x2e4f492e0 | 0xcc00000 |
| 263 | blk.21.ffn_gate_exps.weight | 0x2f1b492e0 | 0x9d80000 |
| 264 | blk.21.ffn_gate_inp.weight | 0x2fb8c92e0 | 0x100000 |
| 265 | blk.21.ffn_norm.weight | 0x2fb9c92e0 | 0x2000 |
| 266 | blk.21.ffn_up_exps.weight | 0x2fb9cb2e0 | 0x9d80000 |
| 267 | blk.22.attn_k.weight | 0x30574b2e0 | 0xd2000 |
| 268 | blk.22.attn_k_norm.weight | 0x30581d2e0 | 0x200 |
| 269 | blk.22.attn_norm.weight | 0x30581d4e0 | 0x2000 |
| 270 | blk.22.attn_output.weight | 0x30581f4e0 | 0x880000 |
| 271 | blk.22.attn_q.weight | 0x30609f4e0 | 0x690000 |
| 272 | blk.22.attn_q_norm.weight | 0x30672f4e0 | 0x200 |
| 273 | blk.22.attn_v.weight | 0x30672f6e0 | 0x200000 |
| 274 | blk.22.ffn_down_exps.weight | 0x30692f6e0 | 0xcc00000 |
| 275 | blk.22.ffn_gate_exps.weight | 0x31352f6e0 | 0x9d80000 |
| 276 | blk.22.ffn_gate_inp.weight | 0x31d2af6e0 | 0x100000 |
| 277 | blk.22.ffn_norm.weight | 0x31d3af6e0 | 0x2000 |
| 278 | blk.22.ffn_up_exps.weight | 0x31d3b16e0 | 0x9d80000 |
| 279 | blk.23.attn_k.weight | 0x3271316e0 | 0xd2000 |
| 280 | blk.23.attn_k_norm.weight | 0x3272036e0 | 0x200 |
| 281 | blk.23.attn_norm.weight | 0x3272038e0 | 0x2000 |
| 282 | blk.23.attn_output.weight | 0x3272058e0 | 0x880000 |
| 283 | blk.23.attn_q.weight | 0x327a858e0 | 0x690000 |
| 284 | blk.23.attn_q_norm.weight | 0x3281158e0 | 0x200 |
| 285 | blk.23.attn_v.weight | 0x328115ae0 | 0x200000 |
| 286 | blk.23.ffn_down_exps.weight | 0x328315ae0 | 0xcc00000 |
| 287 | blk.23.ffn_gate_exps.weight | 0x334f15ae0 | 0x9d80000 |
| 288 | blk.23.ffn_gate_inp.weight | 0x33ec95ae0 | 0x100000 |
| 289 | blk.23.ffn_norm.weight | 0x33ed95ae0 | 0x2000 |
| 290 | blk.23.ffn_up_exps.weight | 0x33ed97ae0 | 0x9d80000 |
| 291 | blk.24.attn_k.weight | 0x348b17ae0 | 0x110000 |
| 292 | blk.24.attn_k_norm.weight | 0x348c27ae0 | 0x200 |
| 293 | blk.24.attn_norm.weight | 0x348c27ce0 | 0x2000 |
| 294 | blk.24.attn_output.weight | 0x348c29ce0 | 0x880000 |
| 295 | blk.24.attn_q.weight | 0x3494a9ce0 | 0x880000 |
| 296 | blk.24.attn_q_norm.weight | 0x349d29ce0 | 0x200 |
| 297 | blk.24.attn_v.weight | 0x349d29ee0 | 0x200000 |
| 298 | blk.24.ffn_down_exps.weight | 0x349f29ee0 | 0xcc00000 |
| 299 | blk.24.ffn_gate_exps.weight | 0x356b29ee0 | 0x9d80000 |
| 300 | blk.24.ffn_gate_inp.weight | 0x3608a9ee0 | 0x100000 |
| 301 | blk.24.ffn_norm.weight | 0x3609a9ee0 | 0x2000 |
| 302 | blk.24.ffn_up_exps.weight | 0x3609abee0 | 0x9d80000 |
| 303 | blk.25.attn_k.weight | 0x36a72bee0 | 0x110000 |
| 304 | blk.25.attn_k_norm.weight | 0x36a83bee0 | 0x200 |
| 305 | blk.25.attn_norm.weight | 0x36a83c0e0 | 0x2000 |
| 306 | blk.25.attn_output.weight | 0x36a83e0e0 | 0x880000 |
| 307 | blk.25.attn_q.weight | 0x36b0be0e0 | 0x880000 |
| 308 | blk.25.attn_q_norm.weight | 0x36b93e0e0 | 0x200 |
| 309 | blk.25.attn_v.weight | 0x36b93e2e0 | 0x200000 |
| 310 | blk.25.ffn_down_exps.weight | 0x36bb3e2e0 | 0xcc00000 |
| 311 | blk.25.ffn_gate_exps.weight | 0x37873e2e0 | 0xcc00000 |
| 312 | blk.25.ffn_gate_inp.weight | 0x38533e2e0 | 0x100000 |
| 313 | blk.25.ffn_norm.weight | 0x38543e2e0 | 0x2000 |
| 314 | blk.25.ffn_up_exps.weight | 0x3854402e0 | 0xcc00000 |
| 315 | blk.26.attn_k.weight | 0x3920402e0 | 0x110000 |
| 316 | blk.26.attn_k_norm.weight | 0x3921502e0 | 0x200 |
| 317 | blk.26.attn_norm.weight | 0x3921504e0 | 0x2000 |
| 318 | blk.26.attn_output.weight | 0x3921524e0 | 0x880000 |
| 319 | blk.26.attn_q.weight | 0x3929d24e0 | 0x880000 |
| 320 | blk.26.attn_q_norm.weight | 0x3932524e0 | 0x200 |
| 321 | blk.26.attn_v.weight | 0x3932526e0 | 0x200000 |
| 322 | blk.26.ffn_down_exps.weight | 0x3934526e0 | 0xcc00000 |
| 323 | blk.26.ffn_gate_exps.weight | 0x3a00526e0 | 0xcc00000 |
| 324 | blk.26.ffn_gate_inp.weight | 0x3acc526e0 | 0x100000 |
| 325 | blk.26.ffn_norm.weight | 0x3acd526e0 | 0x2000 |
| 326 | blk.26.ffn_up_exps.weight | 0x3acd546e0 | 0xcc00000 |
| 327 | blk.27.attn_k.weight | 0x3b99546e0 | 0x110000 |
| 328 | blk.27.attn_k_norm.weight | 0x3b9a646e0 | 0x200 |
| 329 | blk.27.attn_norm.weight | 0x3b9a648e0 | 0x2000 |
| 330 | blk.27.attn_output.weight | 0x3b9a668e0 | 0x880000 |
| 331 | blk.27.attn_q.weight | 0x3ba2e68e0 | 0x880000 |
| 332 | blk.27.attn_q_norm.weight | 0x3bab668e0 | 0x200 |
| 333 | blk.27.attn_v.weight | 0x3bab66ae0 | 0x200000 |
| 334 | blk.27.ffn_down_exps.weight | 0x3bad66ae0 | 0xcc00000 |
| 335 | blk.27.ffn_gate_exps.weight | 0x3c7966ae0 | 0xcc00000 |
| 336 | blk.27.ffn_gate_inp.weight | 0x3d4566ae0 | 0x100000 |
| 337 | blk.27.ffn_norm.weight | 0x3d4666ae0 | 0x2000 |
| 338 | blk.27.ffn_up_exps.weight | 0x3d4668ae0 | 0xcc00000 |
| 339 | blk.28.attn_k.weight | 0x3e1268ae0 | 0x110000 |
| 340 | blk.28.attn_k_norm.weight | 0x3e1378ae0 | 0x200 |
| 341 | blk.28.attn_norm.weight | 0x3e1378ce0 | 0x2000 |
| 342 | blk.28.attn_output.weight | 0x3e137ace0 | 0x880000 |
| 343 | blk.28.attn_q.weight | 0x3e1bface0 | 0x880000 |
| 344 | blk.28.attn_q_norm.weight | 0x3e247ace0 | 0x200 |
| 345 | blk.28.attn_v.weight | 0x3e247aee0 | 0x200000 |
| 346 | blk.28.ffn_down_exps.weight | 0x3e267aee0 | 0xcc00000 |
| 347 | blk.28.ffn_gate_exps.weight | 0x3ef27aee0 | 0xcc00000 |
| 348 | blk.28.ffn_gate_inp.weight | 0x3fbe7aee0 | 0x100000 |
| 349 | blk.28.ffn_norm.weight | 0x3fbf7aee0 | 0x2000 |
| 350 | blk.28.ffn_up_exps.weight | 0x3fbf7cee0 | 0xcc00000 |
| 351 | blk.29.attn_k.weight | 0x408b7cee0 | 0x110000 |
| 352 | blk.29.attn_k_norm.weight | 0x408c8cee0 | 0x200 |
| 353 | blk.29.attn_norm.weight | 0x408c8d0e0 | 0x2000 |
| 354 | blk.29.attn_output.weight | 0x408c8f0e0 | 0x880000 |
| 355 | blk.29.attn_q.weight | 0x40950f0e0 | 0x880000 |
| 356 | blk.29.attn_q_norm.weight | 0x409d8f0e0 | 0x200 |
| 357 | blk.29.attn_v.weight | 0x409d8f2e0 | 0x200000 |
| 358 | blk.29.ffn_down_exps.weight | 0x409f8f2e0 | 0xcc00000 |
| 359 | blk.29.ffn_gate_exps.weight | 0x416b8f2e0 | 0xcc00000 |
| 360 | blk.29.ffn_gate_inp.weight | 0x42378f2e0 | 0x100000 |
| 361 | blk.29.ffn_norm.weight | 0x42388f2e0 | 0x2000 |
| 362 | blk.29.ffn_up_exps.weight | 0x4238912e0 | 0xcc00000 |
| 363 | blk.30.attn_k.weight | 0x4304912e0 | 0x110000 |
| 364 | blk.30.attn_k_norm.weight | 0x4305a12e0 | 0x200 |
| 365 | blk.30.attn_norm.weight | 0x4305a14e0 | 0x2000 |
| 366 | blk.30.attn_output.weight | 0x4305a34e0 | 0x880000 |
| 367 | blk.30.attn_q.weight | 0x430e234e0 | 0x880000 |
| 368 | blk.30.attn_q_norm.weight | 0x4316a34e0 | 0x200 |
| 369 | blk.30.attn_v.weight | 0x4316a36e0 | 0x200000 |
| 370 | blk.30.ffn_down_exps.weight | 0x4318a36e0 | 0xcc00000 |
| 371 | blk.30.ffn_gate_exps.weight | 0x43e4a36e0 | 0xcc00000 |
| 372 | blk.30.ffn_gate_inp.weight | 0x44b0a36e0 | 0x100000 |
| 373 | blk.30.ffn_norm.weight | 0x44b1a36e0 | 0x2000 |
| 374 | blk.30.ffn_up_exps.weight | 0x44b1a56e0 | 0xcc00000 |
| 375 | blk.31.attn_k.weight | 0x457da56e0 | 0x110000 |
| 376 | blk.31.attn_k_norm.weight | 0x457eb56e0 | 0x200 |
| 377 | blk.31.attn_norm.weight | 0x457eb58e0 | 0x2000 |
| 378 | blk.31.attn_output.weight | 0x457eb78e0 | 0x880000 |
| 379 | blk.31.attn_q.weight | 0x4587378e0 | 0x880000 |
| 380 | blk.31.attn_q_norm.weight | 0x458fb78e0 | 0x200 |
| 381 | blk.31.attn_v.weight | 0x458fb7ae0 | 0x200000 |
| 382 | blk.31.ffn_down_exps.weight | 0x4591b7ae0 | 0xcc00000 |
| 383 | blk.31.ffn_gate_exps.weight | 0x465db7ae0 | 0xcc00000 |
| 384 | blk.31.ffn_gate_inp.weight | 0x4729b7ae0 | 0x100000 |
| 385 | blk.31.ffn_norm.weight | 0x472ab7ae0 | 0x2000 |
| 386 | blk.31.ffn_up_exps.weight | 0x472ab9ae0 | 0xcc00000 |
| 387 | blk.32.attn_k.weight | 0x47f6b9ae0 | 0x110000 |
| 388 | blk.32.attn_k_norm.weight | 0x47f7c9ae0 | 0x200 |
| 389 | blk.32.attn_norm.weight | 0x47f7c9ce0 | 0x2000 |
| 390 | blk.32.attn_output.weight | 0x47f7cbce0 | 0x880000 |
| 391 | blk.32.attn_q.weight | 0x48004bce0 | 0x880000 |
| 392 | blk.32.attn_q_norm.weight | 0x4808cbce0 | 0x200 |
| 393 | blk.32.attn_v.weight | 0x4808cbee0 | 0x200000 |
| 394 | blk.32.ffn_down_exps.weight | 0x480acbee0 | 0xcc00000 |
| 395 | blk.32.ffn_gate_exps.weight | 0x48d6cbee0 | 0xcc00000 |
| 396 | blk.32.ffn_gate_inp.weight | 0x49a2cbee0 | 0x100000 |
| 397 | blk.32.ffn_norm.weight | 0x49a3cbee0 | 0x2000 |
| 398 | blk.32.ffn_up_exps.weight | 0x49a3cdee0 | 0xcc00000 |
| 399 | blk.33.attn_k.weight | 0x4a6fcdee0 | 0x110000 |
| 400 | blk.33.attn_k_norm.weight | 0x4a70ddee0 | 0x200 |
| 401 | blk.33.attn_norm.weight | 0x4a70de0e0 | 0x2000 |
| 402 | blk.33.attn_output.weight | 0x4a70e00e0 | 0x880000 |
| 403 | blk.33.attn_q.weight | 0x4a79600e0 | 0x880000 |
| 404 | blk.33.attn_q_norm.weight | 0x4a81e00e0 | 0x200 |
| 405 | blk.33.attn_v.weight | 0x4a81e02e0 | 0x200000 |
| 406 | blk.33.ffn_down_exps.weight | 0x4a83e02e0 | 0xcc00000 |
| 407 | blk.33.ffn_gate_exps.weight | 0x4b4fe02e0 | 0xcc00000 |
| 408 | blk.33.ffn_gate_inp.weight | 0x4c1be02e0 | 0x100000 |
| 409 | blk.33.ffn_norm.weight | 0x4c1ce02e0 | 0x2000 |
| 410 | blk.33.ffn_up_exps.weight | 0x4c1ce22e0 | 0xcc00000 |
| 411 | blk.34.attn_k.weight | 0x4ce8e22e0 | 0x110000 |
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| 413 | blk.34.attn_norm.weight | 0x4ce9f24e0 | 0x2000 |
| 414 | blk.34.attn_output.weight | 0x4ce9f44e0 | 0x880000 |
| 415 | blk.34.attn_q.weight | 0x4cf2744e0 | 0x880000 |
| 416 | blk.34.attn_q_norm.weight | 0x4cfaf44e0 | 0x200 |
| 417 | blk.34.attn_v.weight | 0x4cfaf46e0 | 0x200000 |
| 418 | blk.34.ffn_down_exps.weight | 0x4cfcf46e0 | 0xcc00000 |
| 419 | blk.34.ffn_gate_exps.weight | 0x4dc8f46e0 | 0xcc00000 |
| 420 | blk.34.ffn_gate_inp.weight | 0x4e94f46e0 | 0x100000 |
| 421 | blk.34.ffn_norm.weight | 0x4e95f46e0 | 0x2000 |
| 422 | blk.34.ffn_up_exps.weight | 0x4e95f66e0 | 0xcc00000 |
| 423 | blk.35.attn_k.weight | 0x4f61f66e0 | 0x110000 |
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| 425 | blk.35.attn_norm.weight | 0x4f63068e0 | 0x2000 |
| 426 | blk.35.attn_output.weight | 0x4f63088e0 | 0x880000 |
| 427 | blk.35.attn_q.weight | 0x4f6b888e0 | 0x880000 |
| 428 | blk.35.attn_q_norm.weight | 0x4f74088e0 | 0x200 |
| 429 | blk.35.attn_v.weight | 0x4f7408ae0 | 0x200000 |
| 430 | blk.35.ffn_down_exps.weight | 0x4f7608ae0 | 0xcc00000 |
| 431 | blk.35.ffn_gate_exps.weight | 0x504208ae0 | 0xcc00000 |
| 432 | blk.35.ffn_gate_inp.weight | 0x510e08ae0 | 0x100000 |
| 433 | blk.35.ffn_norm.weight | 0x510f08ae0 | 0x2000 |
| 434 | blk.35.ffn_up_exps.weight | 0x510f0aae0 | 0xcc00000 |
| 435 | blk.36.attn_k.weight | 0x51db0aae0 | 0x110000 |
| 436 | blk.36.attn_k_norm.weight | 0x51dc1aae0 | 0x200 |
| 437 | blk.36.attn_norm.weight | 0x51dc1ace0 | 0x2000 |
| 438 | blk.36.attn_output.weight | 0x51dc1cce0 | 0x880000 |
| 439 | blk.36.attn_q.weight | 0x51e49cce0 | 0x880000 |
| 440 | blk.36.attn_q_norm.weight | 0x51ed1cce0 | 0x200 |
| 441 | blk.36.attn_v.weight | 0x51ed1cee0 | 0x200000 |
| 442 | blk.36.ffn_down_exps.weight | 0x51ef1cee0 | 0xcc00000 |
| 443 | blk.36.ffn_gate_exps.weight | 0x52bb1cee0 | 0xcc00000 |
| 444 | blk.36.ffn_gate_inp.weight | 0x53871cee0 | 0x100000 |
| 445 | blk.36.ffn_norm.weight | 0x53881cee0 | 0x2000 |
| 446 | blk.36.ffn_up_exps.weight | 0x53881eee0 | 0xcc00000 |
| 447 | blk.37.attn_k.weight | 0x54541eee0 | 0x110000 |
| 448 | blk.37.attn_k_norm.weight | 0x54552eee0 | 0x200 |
| 449 | blk.37.attn_norm.weight | 0x54552f0e0 | 0x2000 |
| 450 | blk.37.attn_output.weight | 0x5455310e0 | 0x880000 |
| 451 | blk.37.attn_q.weight | 0x545db10e0 | 0x880000 |
| 452 | blk.37.attn_q_norm.weight | 0x5466310e0 | 0x200 |
| 453 | blk.37.attn_v.weight | 0x5466312e0 | 0x200000 |
| 454 | blk.37.ffn_down_exps.weight | 0x5468312e0 | 0xcc00000 |
| 455 | blk.37.ffn_gate_exps.weight | 0x5534312e0 | 0xcc00000 |
| 456 | blk.37.ffn_gate_inp.weight | 0x5600312e0 | 0x100000 |
| 457 | blk.37.ffn_norm.weight | 0x5601312e0 | 0x2000 |
| 458 | blk.37.ffn_up_exps.weight | 0x5601332e0 | 0xcc00000 |
| 459 | blk.38.attn_k.weight | 0x56cd332e0 | 0x110000 |
| 460 | blk.38.attn_k_norm.weight | 0x56ce432e0 | 0x200 |
| 461 | blk.38.attn_norm.weight | 0x56ce434e0 | 0x2000 |
| 462 | blk.38.attn_output.weight | 0x56ce454e0 | 0x880000 |
| 463 | blk.38.attn_q.weight | 0x56d6c54e0 | 0x880000 |
| 464 | blk.38.attn_q_norm.weight | 0x56df454e0 | 0x200 |
| 465 | blk.38.attn_v.weight | 0x56df456e0 | 0x200000 |
| 466 | blk.38.ffn_down_exps.weight | 0x56e1456e0 | 0xcc00000 |
| 467 | blk.38.ffn_gate_exps.weight | 0x57ad456e0 | 0xcc00000 |
| 468 | blk.38.ffn_gate_inp.weight | 0x5879456e0 | 0x100000 |
| 469 | blk.38.ffn_norm.weight | 0x587a456e0 | 0x2000 |
| 470 | blk.38.ffn_up_exps.weight | 0x587a476e0 | 0xcc00000 |
| 471 | blk.39.attn_k.weight | 0x5946476e0 | 0x110000 |
| 472 | blk.39.attn_k_norm.weight | 0x5947576e0 | 0x200 |
| 473 | blk.39.attn_norm.weight | 0x5947578e0 | 0x2000 |
| 474 | blk.39.attn_output.weight | 0x5947598e0 | 0x880000 |
| 475 | blk.39.attn_q.weight | 0x594fd98e0 | 0x880000 |
| 476 | blk.39.attn_q_norm.weight | 0x5958598e0 | 0x200 |
| 477 | blk.39.attn_v.weight | 0x595859ae0 | 0x200000 |
| 478 | blk.39.ffn_down_exps.weight | 0x595a59ae0 | 0xcc00000 |
| 479 | blk.39.ffn_gate_exps.weight | 0x5a2659ae0 | 0xcc00000 |
| 480 | blk.39.ffn_gate_inp.weight | 0x5af259ae0 | 0x100000 |
| 481 | blk.39.ffn_norm.weight | 0x5af359ae0 | 0x2000 |
| 482 | blk.39.ffn_up_exps.weight | 0x5af35bae0 | 0xcc00000 |
| 483 | blk.40.attn_k.weight | 0x5bbf5bae0 | 0x110000 |
| 484 | blk.40.attn_k_norm.weight | 0x5bc06bae0 | 0x200 |
| 485 | blk.40.attn_norm.weight | 0x5bc06bce0 | 0x2000 |
| 486 | blk.40.attn_output.weight | 0x5bc06dce0 | 0x880000 |
| 487 | blk.40.attn_q.weight | 0x5bc8edce0 | 0x880000 |
| 488 | blk.40.attn_q_norm.weight | 0x5bd16dce0 | 0x200 |
| 489 | blk.40.attn_v.weight | 0x5bd16dee0 | 0x200000 |
| 490 | blk.40.ffn_down_exps.weight | 0x5bd36dee0 | 0xcc00000 |
| 491 | blk.40.ffn_gate_exps.weight | 0x5c9f6dee0 | 0xcc00000 |
| 492 | blk.40.ffn_gate_inp.weight | 0x5d6b6dee0 | 0x100000 |
| 493 | blk.40.ffn_norm.weight | 0x5d6c6dee0 | 0x2000 |
| 494 | blk.40.ffn_up_exps.weight | 0x5d6c6fee0 | 0xcc00000 |
| 495 | blk.41.attn_k.weight | 0x5e386fee0 | 0x110000 |
| 496 | blk.41.attn_k_norm.weight | 0x5e397fee0 | 0x200 |
| 497 | blk.41.attn_norm.weight | 0x5e39800e0 | 0x2000 |
| 498 | blk.41.attn_output.weight | 0x5e39820e0 | 0x880000 |
| 499 | blk.41.attn_q.weight | 0x5e42020e0 | 0x880000 |
| 500 | blk.41.attn_q_norm.weight | 0x5e4a820e0 | 0x200 |
| 501 | blk.41.attn_v.weight | 0x5e4a822e0 | 0x200000 |
| 502 | blk.41.ffn_down_exps.weight | 0x5e4c822e0 | 0xcc00000 |
| 503 | blk.41.ffn_gate_exps.weight | 0x5f18822e0 | 0xcc00000 |
| 504 | blk.41.ffn_gate_inp.weight | 0x5fe4822e0 | 0x100000 |
| 505 | blk.41.ffn_norm.weight | 0x5fe5822e0 | 0x2000 |
| 506 | blk.41.ffn_up_exps.weight | 0x5fe5842e0 | 0xcc00000 |
| 507 | blk.42.attn_k.weight | 0x60b1842e0 | 0x110000 |
| 508 | blk.42.attn_k_norm.weight | 0x60b2942e0 | 0x200 |
| 509 | blk.42.attn_norm.weight | 0x60b2944e0 | 0x2000 |
| 510 | blk.42.attn_output.weight | 0x60b2964e0 | 0x880000 |
| 511 | blk.42.attn_q.weight | 0x60bb164e0 | 0x880000 |
| 512 | blk.42.attn_q_norm.weight | 0x60c3964e0 | 0x200 |
| 513 | blk.42.attn_v.weight | 0x60c3966e0 | 0x200000 |
| 514 | blk.42.ffn_down_exps.weight | 0x60c5966e0 | 0xcc00000 |
| 515 | blk.42.ffn_gate_exps.weight | 0x6191966e0 | 0xcc00000 |
| 516 | blk.42.ffn_gate_inp.weight | 0x625d966e0 | 0x100000 |
| 517 | blk.42.ffn_norm.weight | 0x625e966e0 | 0x2000 |
| 518 | blk.42.ffn_up_exps.weight | 0x625e986e0 | 0xcc00000 |
| 519 | blk.43.attn_k.weight | 0x632a986e0 | 0x110000 |
| 520 | blk.43.attn_k_norm.weight | 0x632ba86e0 | 0x200 |
| 521 | blk.43.attn_norm.weight | 0x632ba88e0 | 0x2000 |
| 522 | blk.43.attn_output.weight | 0x632baa8e0 | 0x880000 |
| 523 | blk.43.attn_q.weight | 0x63342a8e0 | 0x880000 |
| 524 | blk.43.attn_q_norm.weight | 0x633caa8e0 | 0x200 |
| 525 | blk.43.attn_v.weight | 0x633caaae0 | 0x200000 |
| 526 | blk.43.ffn_down_exps.weight | 0x633eaaae0 | 0xcc00000 |
| 527 | blk.43.ffn_gate_exps.weight | 0x640aaaae0 | 0xcc00000 |
| 528 | blk.43.ffn_gate_inp.weight | 0x64d6aaae0 | 0x100000 |
| 529 | blk.43.ffn_norm.weight | 0x64d7aaae0 | 0x2000 |
| 530 | blk.43.ffn_up_exps.weight | 0x64d7acae0 | 0xcc00000 |
| 531 | blk.44.attn_k.weight | 0x65a3acae0 | 0x110000 |
| 532 | blk.44.attn_k_norm.weight | 0x65a4bcae0 | 0x200 |
| 533 | blk.44.attn_norm.weight | 0x65a4bcce0 | 0x2000 |
| 534 | blk.44.attn_output.weight | 0x65a4bece0 | 0x880000 |
| 535 | blk.44.attn_q.weight | 0x65ad3ece0 | 0x880000 |
| 536 | blk.44.attn_q_norm.weight | 0x65b5bece0 | 0x200 |
| 537 | blk.44.attn_v.weight | 0x65b5beee0 | 0x200000 |
| 538 | blk.44.ffn_down_exps.weight | 0x65b7beee0 | 0xcc00000 |
| 539 | blk.44.ffn_gate_exps.weight | 0x6683beee0 | 0xcc00000 |
| 540 | blk.44.ffn_gate_inp.weight | 0x674fbeee0 | 0x100000 |
| 541 | blk.44.ffn_norm.weight | 0x6750beee0 | 0x2000 |
| 542 | blk.44.ffn_up_exps.weight | 0x6750c0ee0 | 0xcc00000 |
| 543 | blk.45.attn_k.weight | 0x681cc0ee0 | 0x110000 |
| 544 | blk.45.attn_k_norm.weight | 0x681dd0ee0 | 0x200 |
| 545 | blk.45.attn_norm.weight | 0x681dd10e0 | 0x2000 |
| 546 | blk.45.attn_output.weight | 0x681dd30e0 | 0x880000 |
| 547 | blk.45.attn_q.weight | 0x6826530e0 | 0x880000 |
| 548 | blk.45.attn_q_norm.weight | 0x682ed30e0 | 0x200 |
| 549 | blk.45.attn_v.weight | 0x682ed32e0 | 0x200000 |
| 550 | blk.45.ffn_down_exps.weight | 0x6830d32e0 | 0xcc00000 |
| 551 | blk.45.ffn_gate_exps.weight | 0x68fcd32e0 | 0xcc00000 |
| 552 | blk.45.ffn_gate_inp.weight | 0x69c8d32e0 | 0x100000 |
| 553 | blk.45.ffn_norm.weight | 0x69c9d32e0 | 0x2000 |
| 554 | blk.45.ffn_up_exps.weight | 0x69c9d52e0 | 0xcc00000 |
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 | Q8_0 |
| 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 | Q3_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 7 | blk.0.attn_q.weight | Block 0 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 10 | blk.0.ffn_down_exps.weight | Block 0 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 11 | blk.0.ffn_gate_exps.weight | Block 0 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 19 | blk.1.attn_q.weight | Block 1 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 22 | blk.1.ffn_down_exps.weight | Block 1 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 23 | blk.1.ffn_gate_exps.weight | Block 1 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 31 | blk.2.attn_q.weight | Block 2 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 34 | blk.2.ffn_down_exps.weight | Block 2 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 35 | blk.2.ffn_gate_exps.weight | Block 2 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 43 | blk.3.attn_q.weight | Block 3 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 46 | blk.3.ffn_down_exps.weight | Block 3 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 47 | blk.3.ffn_gate_exps.weight | Block 3 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 55 | blk.4.attn_q.weight | Block 4 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 58 | blk.4.ffn_down_exps.weight | Block 4 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 59 | blk.4.ffn_gate_exps.weight | Block 4 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 67 | blk.5.attn_q.weight | Block 5 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 70 | blk.5.ffn_down_exps.weight | Block 5 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 71 | blk.5.ffn_gate_exps.weight | Block 5 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 79 | blk.6.attn_q.weight | Block 6 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 82 | blk.6.ffn_down_exps.weight | Block 6 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 83 | blk.6.ffn_gate_exps.weight | Block 6 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 91 | blk.7.attn_q.weight | Block 7 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 94 | blk.7.ffn_down_exps.weight | Block 7 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 95 | blk.7.ffn_gate_exps.weight | Block 7 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 103 | blk.8.attn_q.weight | Block 8 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 106 | blk.8.ffn_down_exps.weight | Block 8 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 107 | blk.8.ffn_gate_exps.weight | Block 8 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 115 | blk.9.attn_q.weight | Block 9 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 118 | blk.9.ffn_down_exps.weight | Block 9 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 119 | blk.9.ffn_gate_exps.weight | Block 9 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 127 | blk.10.attn_q.weight | Block 10 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 130 | blk.10.ffn_down_exps.weight | Block 10 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 131 | blk.10.ffn_gate_exps.weight | Block 10 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 139 | blk.11.attn_q.weight | Block 11 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 142 | blk.11.ffn_down_exps.weight | Block 11 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 143 | blk.11.ffn_gate_exps.weight | Block 11 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 151 | blk.12.attn_q.weight | Block 12 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 154 | blk.12.ffn_down_exps.weight | Block 12 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 155 | blk.12.ffn_gate_exps.weight | Block 12 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 163 | blk.13.attn_q.weight | Block 13 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 166 | blk.13.ffn_down_exps.weight | Block 13 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 167 | blk.13.ffn_gate_exps.weight | Block 13 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 175 | blk.14.attn_q.weight | Block 14 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 178 | blk.14.ffn_down_exps.weight | Block 14 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 179 | blk.14.ffn_gate_exps.weight | Block 14 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 187 | blk.15.attn_q.weight | Block 15 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 190 | blk.15.ffn_down_exps.weight | Block 15 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 191 | blk.15.ffn_gate_exps.weight | Block 15 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 199 | blk.16.attn_q.weight | Block 16 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 202 | blk.16.ffn_down_exps.weight | Block 16 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 203 | blk.16.ffn_gate_exps.weight | Block 16 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 211 | blk.17.attn_q.weight | Block 17 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 214 | blk.17.ffn_down_exps.weight | Block 17 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 215 | blk.17.ffn_gate_exps.weight | Block 17 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 223 | blk.18.attn_q.weight | Block 18 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 226 | blk.18.ffn_down_exps.weight | Block 18 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 227 | blk.18.ffn_gate_exps.weight | Block 18 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q6_K |
| 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 | Q8_0 |
| 235 | blk.19.attn_q.weight | Block 19 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 238 | blk.19.ffn_down_exps.weight | Block 19 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 239 | blk.19.ffn_gate_exps.weight | Block 19 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 247 | blk.20.attn_q.weight | Block 20 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 250 | blk.20.ffn_down_exps.weight | Block 20 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 251 | blk.20.ffn_gate_exps.weight | Block 20 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 259 | blk.21.attn_q.weight | Block 21 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 262 | blk.21.ffn_down_exps.weight | Block 21 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 263 | blk.21.ffn_gate_exps.weight | Block 21 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 271 | blk.22.attn_q.weight | Block 22 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 274 | blk.22.ffn_down_exps.weight | Block 22 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 275 | blk.22.ffn_gate_exps.weight | Block 22 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q6_K |
| 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 | Q8_0 |
| 283 | blk.23.attn_q.weight | Block 23 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q6_K |
| 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 | F16 |
| 286 | blk.23.ffn_down_exps.weight | Block 23 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 287 | blk.23.ffn_gate_exps.weight | Block 23 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 295 | blk.24.attn_q.weight | Block 24 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 298 | blk.24.ffn_down_exps.weight | Block 24 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 299 | blk.24.ffn_gate_exps.weight | Block 24 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q6_K |
| 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 | Q6_K |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 307 | blk.25.attn_q.weight | Block 25 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 310 | blk.25.ffn_down_exps.weight | Block 25 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 311 | blk.25.ffn_gate_exps.weight | Block 25 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 319 | blk.26.attn_q.weight | Block 26 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 322 | blk.26.ffn_down_exps.weight | Block 26 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 323 | blk.26.ffn_gate_exps.weight | Block 26 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 331 | blk.27.attn_q.weight | Block 27 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 334 | blk.27.ffn_down_exps.weight | Block 27 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 335 | blk.27.ffn_gate_exps.weight | Block 27 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 343 | blk.28.attn_q.weight | Block 28 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 346 | blk.28.ffn_down_exps.weight | Block 28 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 347 | blk.28.ffn_gate_exps.weight | Block 28 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 355 | blk.29.attn_q.weight | Block 29 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 358 | blk.29.ffn_down_exps.weight | Block 29 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 359 | blk.29.ffn_gate_exps.weight | Block 29 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 367 | blk.30.attn_q.weight | Block 30 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 370 | blk.30.ffn_down_exps.weight | Block 30 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 371 | blk.30.ffn_gate_exps.weight | Block 30 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 379 | blk.31.attn_q.weight | Block 31 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 382 | blk.31.ffn_down_exps.weight | Block 31 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 383 | blk.31.ffn_gate_exps.weight | Block 31 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 391 | blk.32.attn_q.weight | Block 32 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 394 | blk.32.ffn_down_exps.weight | Block 32 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 395 | blk.32.ffn_gate_exps.weight | Block 32 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 403 | blk.33.attn_q.weight | Block 33 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 406 | blk.33.ffn_down_exps.weight | Block 33 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 407 | blk.33.ffn_gate_exps.weight | Block 33 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 415 | blk.34.attn_q.weight | Block 34 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 418 | blk.34.ffn_down_exps.weight | Block 34 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 419 | blk.34.ffn_gate_exps.weight | Block 34 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 427 | blk.35.attn_q.weight | Block 35 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 430 | blk.35.ffn_down_exps.weight | Block 35 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 431 | blk.35.ffn_gate_exps.weight | Block 35 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 439 | blk.36.attn_q.weight | Block 36 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 442 | blk.36.ffn_down_exps.weight | Block 36 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 443 | blk.36.ffn_gate_exps.weight | Block 36 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 451 | blk.37.attn_q.weight | Block 37 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 454 | blk.37.ffn_down_exps.weight | Block 37 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 455 | blk.37.ffn_gate_exps.weight | Block 37 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 463 | blk.38.attn_q.weight | Block 38 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 466 | blk.38.ffn_down_exps.weight | Block 38 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 467 | blk.38.ffn_gate_exps.weight | Block 38 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 475 | blk.39.attn_q.weight | Block 39 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 478 | blk.39.ffn_down_exps.weight | Block 39 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 479 | blk.39.ffn_gate_exps.weight | Block 39 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 487 | blk.40.attn_q.weight | Block 40 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 490 | blk.40.ffn_down_exps.weight | Block 40 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 491 | blk.40.ffn_gate_exps.weight | Block 40 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 499 | blk.41.attn_q.weight | Block 41 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 502 | blk.41.ffn_down_exps.weight | Block 41 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 503 | blk.41.ffn_gate_exps.weight | Block 41 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 511 | blk.42.attn_q.weight | Block 42 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 514 | blk.42.ffn_down_exps.weight | Block 42 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 515 | blk.42.ffn_gate_exps.weight | Block 42 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 523 | blk.43.attn_q.weight | Block 43 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 526 | blk.43.ffn_down_exps.weight | Block 43 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 527 | blk.43.ffn_gate_exps.weight | Block 43 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 535 | blk.44.attn_q.weight | Block 44 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 538 | blk.44.ffn_down_exps.weight | Block 44 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 539 | blk.44.ffn_gate_exps.weight | Block 44 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- 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 | Q8_0 |
| 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 | Q8_0 |
| 547 | blk.45.attn_q.weight | Block 45 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | Q8_0 |
| 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 | F16 |
| 550 | blk.45.ffn_down_exps.weight | Block 45 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q8_0 |
| 551 | blk.45.ffn_gate_exps.weight | Block 45 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | Q8_0 |
| 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 | Q8_0 |
- Total elements in blk.45: (~623M) 623120640
- Percentage of total elements: 2.13%