File size: 188,145 Bytes
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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 | # Qwen3-30B-A3B-IQ4_NL.gguf - GGUF Internal File Dump
- Endian: LITTLE endian
## Key Value Metadata Store
There are 44 key-value pairs in this file
| POS | TYPE | Count | Key | Value |
|----:|:---------|-------:|:------------------------------------------|:--------------------------------------------------------------------|
| 1 | UINT32 | 1 | GGUF.version | 3 |
| 2 | UINT64 | 1 | GGUF.tensor_count | 579 |
| 3 | UINT64 | 1 | GGUF.kv_count | 41 |
| 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.block_count | 48 |
| 17 | UINT32 | 1 | qwen3moe.context_length | 40960 |
| 18 | UINT32 | 1 | qwen3moe.embedding_length | 2048 |
| 19 | UINT32 | 1 | qwen3moe.feed_forward_length | 6144 |
| 20 | UINT32 | 1 | qwen3moe.attention.head_count | 32 |
| 21 | UINT32 | 1 | qwen3moe.attention.head_count_kv | 4 |
| 22 | FLOAT32 | 1 | qwen3moe.rope.freq_base | 1000000.0 |
| 23 | FLOAT32 | 1 | qwen3moe.attention.layer_norm_rms_epsilon | 1e-06 |
| 24 | UINT32 | 1 | qwen3moe.expert_used_count | 8 |
| 25 | UINT32 | 1 | qwen3moe.attention.key_length | 128 |
| 26 | UINT32 | 1 | qwen3moe.attention.value_length | 128 |
| 27 | UINT32 | 1 | qwen3moe.expert_count | 128 |
| 28 | UINT32 | 1 | qwen3moe.expert_feed_forward_length | 768 |
| 29 | STRING | 1 | tokenizer.ggml.model | `gpt2` |
| 30 | STRING | 1 | tokenizer.ggml.pre | `qwen2` |
| 31 | [STRING] | 151936 | tokenizer.ggml.tokens | [ `!`, `"`, `#`, `$`, `%`, ... ] |
| 32 | [INT32] | 151936 | tokenizer.ggml.token_type | [ 1, 1, 1, 1, 1, 1, 1, ... ] |
| 33 | [STRING] | 151387 | tokenizer.ggml.merges | [ `Ġ Ġ`, `ĠĠ ĠĠ`, `i n`, `Ġ t`, `ĠĠĠĠ ĠĠĠĠ`, ... ] |
| 34 | UINT32 | 1 | tokenizer.ggml.eos_token_id | 151645 |
| 35 | UINT32 | 1 | tokenizer.ggml.padding_token_id | 151643 |
| 36 | UINT32 | 1 | tokenizer.ggml.bos_token_id | 151643 |
| 37 | BOOL | 1 | tokenizer.ggml.add_bos_token | False |
| 38 | STRING | 1 | tokenizer.chat_template | `{%- if tools %}{{- '<|im_`...`{%- endif %}{%- endif %}` |
| 39 | UINT32 | 1 | general.quantization_version | 2 |
| 40 | UINT32 | 1 | general.file_type | 25 |
| 41 | STRING | 1 | quantize.imatrix.file | `./imatrix/imatrix-Qwen3-30B-A3B-large.dat` |
| 42 | STRING | 1 | quantize.imatrix.dataset | `../../datasets/imatrix/calibration_all_large.txt` |
| 43 | INT32 | 1 | quantize.imatrix.entries_count | 382 |
| 44 | INT32 | 1 | quantize.imatrix.chunks_count | 4978 |
## Tensors Overview ~31B Elements
Total number of elements in all tensors: 30532122624 Elements
- [Qwen3-30B-A3B-IQ4\_NL.gguf - GGUF Internal File Dump](#qwen3-30b-a3b-iq4_nlgguf---gguf-internal-file-dump)
- [Key Value Metadata Store](#key-value-metadata-store)
- [Tensors Overview ~31B Elements](#tensors-overview-31b-elements)
- [Tensor Data Offset](#tensor-data-offset)
- [Base Tensor Group : ~622M Elements](#base-tensor-group--622m-elements)
- [Block 0 Tensor Group : ~623M Elements](#block-0-tensor-group--623m-elements)
- [Block 1 Tensor Group : ~623M Elements](#block-1-tensor-group--623m-elements)
- [Block 2 Tensor Group : ~623M Elements](#block-2-tensor-group--623m-elements)
- [Block 3 Tensor Group : ~623M Elements](#block-3-tensor-group--623m-elements)
- [Block 4 Tensor Group : ~623M Elements](#block-4-tensor-group--623m-elements)
- [Block 5 Tensor Group : ~623M Elements](#block-5-tensor-group--623m-elements)
- [Block 6 Tensor Group : ~623M Elements](#block-6-tensor-group--623m-elements)
- [Block 7 Tensor Group : ~623M Elements](#block-7-tensor-group--623m-elements)
- [Block 8 Tensor Group : ~623M Elements](#block-8-tensor-group--623m-elements)
- [Block 9 Tensor Group : ~623M Elements](#block-9-tensor-group--623m-elements)
- [Block 10 Tensor Group : ~623M Elements](#block-10-tensor-group--623m-elements)
- [Block 11 Tensor Group : ~623M Elements](#block-11-tensor-group--623m-elements)
- [Block 12 Tensor Group : ~623M Elements](#block-12-tensor-group--623m-elements)
- [Block 13 Tensor Group : ~623M Elements](#block-13-tensor-group--623m-elements)
- [Block 14 Tensor Group : ~623M Elements](#block-14-tensor-group--623m-elements)
- [Block 15 Tensor Group : ~623M Elements](#block-15-tensor-group--623m-elements)
- [Block 16 Tensor Group : ~623M Elements](#block-16-tensor-group--623m-elements)
- [Block 17 Tensor Group : ~623M Elements](#block-17-tensor-group--623m-elements)
- [Block 18 Tensor Group : ~623M Elements](#block-18-tensor-group--623m-elements)
- [Block 19 Tensor Group : ~623M Elements](#block-19-tensor-group--623m-elements)
- [Block 20 Tensor Group : ~623M Elements](#block-20-tensor-group--623m-elements)
- [Block 21 Tensor Group : ~623M Elements](#block-21-tensor-group--623m-elements)
- [Block 22 Tensor Group : ~623M Elements](#block-22-tensor-group--623m-elements)
- [Block 23 Tensor Group : ~623M Elements](#block-23-tensor-group--623m-elements)
- [Block 24 Tensor Group : ~623M Elements](#block-24-tensor-group--623m-elements)
- [Block 25 Tensor Group : ~623M Elements](#block-25-tensor-group--623m-elements)
- [Block 26 Tensor Group : ~623M Elements](#block-26-tensor-group--623m-elements)
- [Block 27 Tensor Group : ~623M Elements](#block-27-tensor-group--623m-elements)
- [Block 28 Tensor Group : ~623M Elements](#block-28-tensor-group--623m-elements)
- [Block 29 Tensor Group : ~623M Elements](#block-29-tensor-group--623m-elements)
- [Block 30 Tensor Group : ~623M Elements](#block-30-tensor-group--623m-elements)
- [Block 31 Tensor Group : ~623M Elements](#block-31-tensor-group--623m-elements)
- [Block 32 Tensor Group : ~623M Elements](#block-32-tensor-group--623m-elements)
- [Block 33 Tensor Group : ~623M Elements](#block-33-tensor-group--623m-elements)
- [Block 34 Tensor Group : ~623M Elements](#block-34-tensor-group--623m-elements)
- [Block 35 Tensor Group : ~623M Elements](#block-35-tensor-group--623m-elements)
- [Block 36 Tensor Group : ~623M Elements](#block-36-tensor-group--623m-elements)
- [Block 37 Tensor Group : ~623M Elements](#block-37-tensor-group--623m-elements)
- [Block 38 Tensor Group : ~623M Elements](#block-38-tensor-group--623m-elements)
- [Block 39 Tensor Group : ~623M Elements](#block-39-tensor-group--623m-elements)
- [Block 40 Tensor Group : ~623M Elements](#block-40-tensor-group--623m-elements)
- [Block 41 Tensor Group : ~623M Elements](#block-41-tensor-group--623m-elements)
- [Block 42 Tensor Group : ~623M Elements](#block-42-tensor-group--623m-elements)
- [Block 43 Tensor Group : ~623M Elements](#block-43-tensor-group--623m-elements)
- [Block 44 Tensor Group : ~623M Elements](#block-44-tensor-group--623m-elements)
- [Block 45 Tensor Group : ~623M Elements](#block-45-tensor-group--623m-elements)
- [Block 46 Tensor Group : ~623M Elements](#block-46-tensor-group--623m-elements)
- [Block 47 Tensor Group : ~623M Elements](#block-47-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 | 0x5b18c0 | 0xa6ec000 |
| 1 | output_norm.weight | 0xac9d8c0 | 0x2000 |
| 2 | token_embd.weight | 0xac9f8c0 | 0x7f82800 |
| 3 | blk.0.attn_k.weight | 0x12c220c0 | 0x6e000 |
| 4 | blk.0.attn_k_norm.weight | 0x12c900c0 | 0x200 |
| 5 | blk.0.attn_norm.weight | 0x12c902c0 | 0x2000 |
| 6 | blk.0.attn_output.weight | 0x12c922c0 | 0x480000 |
| 7 | blk.0.attn_q.weight | 0x131122c0 | 0x370000 |
| 8 | blk.0.attn_q_norm.weight | 0x134822c0 | 0x200 |
| 9 | blk.0.attn_v.weight | 0x134824c0 | 0x88000 |
| 10 | blk.0.ffn_down_exps.weight | 0x1350a4c0 | 0x8400000 |
| 11 | blk.0.ffn_gate_exps.weight | 0x1b90a4c0 | 0x5280000 |
| 12 | blk.0.ffn_gate_inp.weight | 0x20b8a4c0 | 0x100000 |
| 13 | blk.0.ffn_norm.weight | 0x20c8a4c0 | 0x2000 |
| 14 | blk.0.ffn_up_exps.weight | 0x20c8c4c0 | 0x5280000 |
| 15 | blk.1.attn_k.weight | 0x25f0c4c0 | 0x6e000 |
| 16 | blk.1.attn_k_norm.weight | 0x25f7a4c0 | 0x200 |
| 17 | blk.1.attn_norm.weight | 0x25f7a6c0 | 0x2000 |
| 18 | blk.1.attn_output.weight | 0x25f7c6c0 | 0x480000 |
| 19 | blk.1.attn_q.weight | 0x263fc6c0 | 0x370000 |
| 20 | blk.1.attn_q_norm.weight | 0x2676c6c0 | 0x200 |
| 21 | blk.1.attn_v.weight | 0x2676c8c0 | 0x88000 |
| 22 | blk.1.ffn_down_exps.weight | 0x267f48c0 | 0x8400000 |
| 23 | blk.1.ffn_gate_exps.weight | 0x2ebf48c0 | 0x5280000 |
| 24 | blk.1.ffn_gate_inp.weight | 0x33e748c0 | 0x100000 |
| 25 | blk.1.ffn_norm.weight | 0x33f748c0 | 0x2000 |
| 26 | blk.1.ffn_up_exps.weight | 0x33f768c0 | 0x5280000 |
| 27 | blk.2.attn_k.weight | 0x391f68c0 | 0x6e000 |
| 28 | blk.2.attn_k_norm.weight | 0x392648c0 | 0x200 |
| 29 | blk.2.attn_norm.weight | 0x39264ac0 | 0x2000 |
| 30 | blk.2.attn_output.weight | 0x39266ac0 | 0x480000 |
| 31 | blk.2.attn_q.weight | 0x396e6ac0 | 0x370000 |
| 32 | blk.2.attn_q_norm.weight | 0x39a56ac0 | 0x200 |
| 33 | blk.2.attn_v.weight | 0x39a56cc0 | 0x88000 |
| 34 | blk.2.ffn_down_exps.weight | 0x39adecc0 | 0x8400000 |
| 35 | blk.2.ffn_gate_exps.weight | 0x41edecc0 | 0x5280000 |
| 36 | blk.2.ffn_gate_inp.weight | 0x4715ecc0 | 0x100000 |
| 37 | blk.2.ffn_norm.weight | 0x4725ecc0 | 0x2000 |
| 38 | blk.2.ffn_up_exps.weight | 0x47260cc0 | 0x5280000 |
| 39 | blk.3.attn_k.weight | 0x4c4e0cc0 | 0x6e000 |
| 40 | blk.3.attn_k_norm.weight | 0x4c54ecc0 | 0x200 |
| 41 | blk.3.attn_norm.weight | 0x4c54eec0 | 0x2000 |
| 42 | blk.3.attn_output.weight | 0x4c550ec0 | 0x480000 |
| 43 | blk.3.attn_q.weight | 0x4c9d0ec0 | 0x370000 |
| 44 | blk.3.attn_q_norm.weight | 0x4cd40ec0 | 0x200 |
| 45 | blk.3.attn_v.weight | 0x4cd410c0 | 0x88000 |
| 46 | blk.3.ffn_down_exps.weight | 0x4cdc90c0 | 0x8400000 |
| 47 | blk.3.ffn_gate_exps.weight | 0x551c90c0 | 0x5280000 |
| 48 | blk.3.ffn_gate_inp.weight | 0x5a4490c0 | 0x100000 |
| 49 | blk.3.ffn_norm.weight | 0x5a5490c0 | 0x2000 |
| 50 | blk.3.ffn_up_exps.weight | 0x5a54b0c0 | 0x5280000 |
| 51 | blk.4.attn_k.weight | 0x5f7cb0c0 | 0x6e000 |
| 52 | blk.4.attn_k_norm.weight | 0x5f8390c0 | 0x200 |
| 53 | blk.4.attn_norm.weight | 0x5f8392c0 | 0x2000 |
| 54 | blk.4.attn_output.weight | 0x5f83b2c0 | 0x480000 |
| 55 | blk.4.attn_q.weight | 0x5fcbb2c0 | 0x370000 |
| 56 | blk.4.attn_q_norm.weight | 0x6002b2c0 | 0x200 |
| 57 | blk.4.attn_v.weight | 0x6002b4c0 | 0x88000 |
| 58 | blk.4.ffn_down_exps.weight | 0x600b34c0 | 0x8400000 |
| 59 | blk.4.ffn_gate_exps.weight | 0x684b34c0 | 0x5280000 |
| 60 | blk.4.ffn_gate_inp.weight | 0x6d7334c0 | 0x100000 |
| 61 | blk.4.ffn_norm.weight | 0x6d8334c0 | 0x2000 |
| 62 | blk.4.ffn_up_exps.weight | 0x6d8354c0 | 0x5280000 |
| 63 | blk.5.attn_k.weight | 0x72ab54c0 | 0x6e000 |
| 64 | blk.5.attn_k_norm.weight | 0x72b234c0 | 0x200 |
| 65 | blk.5.attn_norm.weight | 0x72b236c0 | 0x2000 |
| 66 | blk.5.attn_output.weight | 0x72b256c0 | 0x480000 |
| 67 | blk.5.attn_q.weight | 0x72fa56c0 | 0x370000 |
| 68 | blk.5.attn_q_norm.weight | 0x733156c0 | 0x200 |
| 69 | blk.5.attn_v.weight | 0x733158c0 | 0x88000 |
| 70 | blk.5.ffn_down_exps.weight | 0x7339d8c0 | 0x8400000 |
| 71 | blk.5.ffn_gate_exps.weight | 0x7b79d8c0 | 0x5280000 |
| 72 | blk.5.ffn_gate_inp.weight | 0x80a1d8c0 | 0x100000 |
| 73 | blk.5.ffn_norm.weight | 0x80b1d8c0 | 0x2000 |
| 74 | blk.5.ffn_up_exps.weight | 0x80b1f8c0 | 0x5280000 |
| 75 | blk.6.attn_k.weight | 0x85d9f8c0 | 0x6e000 |
| 76 | blk.6.attn_k_norm.weight | 0x85e0d8c0 | 0x200 |
| 77 | blk.6.attn_norm.weight | 0x85e0dac0 | 0x2000 |
| 78 | blk.6.attn_output.weight | 0x85e0fac0 | 0x480000 |
| 79 | blk.6.attn_q.weight | 0x8628fac0 | 0x370000 |
| 80 | blk.6.attn_q_norm.weight | 0x865ffac0 | 0x200 |
| 81 | blk.6.attn_v.weight | 0x865ffcc0 | 0x88000 |
| 82 | blk.6.ffn_down_exps.weight | 0x86687cc0 | 0x8400000 |
| 83 | blk.6.ffn_gate_exps.weight | 0x8ea87cc0 | 0x5280000 |
| 84 | blk.6.ffn_gate_inp.weight | 0x93d07cc0 | 0x100000 |
| 85 | blk.6.ffn_norm.weight | 0x93e07cc0 | 0x2000 |
| 86 | blk.6.ffn_up_exps.weight | 0x93e09cc0 | 0x5280000 |
| 87 | blk.7.attn_k.weight | 0x99089cc0 | 0x6e000 |
| 88 | blk.7.attn_k_norm.weight | 0x990f7cc0 | 0x200 |
| 89 | blk.7.attn_norm.weight | 0x990f7ec0 | 0x2000 |
| 90 | blk.7.attn_output.weight | 0x990f9ec0 | 0x480000 |
| 91 | blk.7.attn_q.weight | 0x99579ec0 | 0x370000 |
| 92 | blk.7.attn_q_norm.weight | 0x998e9ec0 | 0x200 |
| 93 | blk.7.attn_v.weight | 0x998ea0c0 | 0x88000 |
| 94 | blk.7.ffn_down_exps.weight | 0x999720c0 | 0x8400000 |
| 95 | blk.7.ffn_gate_exps.weight | 0xa1d720c0 | 0x5280000 |
| 96 | blk.7.ffn_gate_inp.weight | 0xa6ff20c0 | 0x100000 |
| 97 | blk.7.ffn_norm.weight | 0xa70f20c0 | 0x2000 |
| 98 | blk.7.ffn_up_exps.weight | 0xa70f40c0 | 0x5280000 |
| 99 | blk.8.attn_k.weight | 0xac3740c0 | 0x6e000 |
| 100 | blk.8.attn_k_norm.weight | 0xac3e20c0 | 0x200 |
| 101 | blk.8.attn_norm.weight | 0xac3e22c0 | 0x2000 |
| 102 | blk.8.attn_output.weight | 0xac3e42c0 | 0x480000 |
| 103 | blk.8.attn_q.weight | 0xac8642c0 | 0x370000 |
| 104 | blk.8.attn_q_norm.weight | 0xacbd42c0 | 0x200 |
| 105 | blk.8.attn_v.weight | 0xacbd44c0 | 0x88000 |
| 106 | blk.8.ffn_down_exps.weight | 0xacc5c4c0 | 0x8400000 |
| 107 | blk.8.ffn_gate_exps.weight | 0xb505c4c0 | 0x5280000 |
| 108 | blk.8.ffn_gate_inp.weight | 0xba2dc4c0 | 0x100000 |
| 109 | blk.8.ffn_norm.weight | 0xba3dc4c0 | 0x2000 |
| 110 | blk.8.ffn_up_exps.weight | 0xba3de4c0 | 0x5280000 |
| 111 | blk.9.attn_k.weight | 0xbf65e4c0 | 0x6e000 |
| 112 | blk.9.attn_k_norm.weight | 0xbf6cc4c0 | 0x200 |
| 113 | blk.9.attn_norm.weight | 0xbf6cc6c0 | 0x2000 |
| 114 | blk.9.attn_output.weight | 0xbf6ce6c0 | 0x480000 |
| 115 | blk.9.attn_q.weight | 0xbfb4e6c0 | 0x370000 |
| 116 | blk.9.attn_q_norm.weight | 0xbfebe6c0 | 0x200 |
| 117 | blk.9.attn_v.weight | 0xbfebe8c0 | 0x88000 |
| 118 | blk.9.ffn_down_exps.weight | 0xbff468c0 | 0x8400000 |
| 119 | blk.9.ffn_gate_exps.weight | 0xc83468c0 | 0x5280000 |
| 120 | blk.9.ffn_gate_inp.weight | 0xcd5c68c0 | 0x100000 |
| 121 | blk.9.ffn_norm.weight | 0xcd6c68c0 | 0x2000 |
| 122 | blk.9.ffn_up_exps.weight | 0xcd6c88c0 | 0x5280000 |
| 123 | blk.10.attn_k.weight | 0xd29488c0 | 0x6e000 |
| 124 | blk.10.attn_k_norm.weight | 0xd29b68c0 | 0x200 |
| 125 | blk.10.attn_norm.weight | 0xd29b6ac0 | 0x2000 |
| 126 | blk.10.attn_output.weight | 0xd29b8ac0 | 0x480000 |
| 127 | blk.10.attn_q.weight | 0xd2e38ac0 | 0x370000 |
| 128 | blk.10.attn_q_norm.weight | 0xd31a8ac0 | 0x200 |
| 129 | blk.10.attn_v.weight | 0xd31a8cc0 | 0x88000 |
| 130 | blk.10.ffn_down_exps.weight | 0xd3230cc0 | 0x8400000 |
| 131 | blk.10.ffn_gate_exps.weight | 0xdb630cc0 | 0x5280000 |
| 132 | blk.10.ffn_gate_inp.weight | 0xe08b0cc0 | 0x100000 |
| 133 | blk.10.ffn_norm.weight | 0xe09b0cc0 | 0x2000 |
| 134 | blk.10.ffn_up_exps.weight | 0xe09b2cc0 | 0x5280000 |
| 135 | blk.11.attn_k.weight | 0xe5c32cc0 | 0x6e000 |
| 136 | blk.11.attn_k_norm.weight | 0xe5ca0cc0 | 0x200 |
| 137 | blk.11.attn_norm.weight | 0xe5ca0ec0 | 0x2000 |
| 138 | blk.11.attn_output.weight | 0xe5ca2ec0 | 0x480000 |
| 139 | blk.11.attn_q.weight | 0xe6122ec0 | 0x370000 |
| 140 | blk.11.attn_q_norm.weight | 0xe6492ec0 | 0x200 |
| 141 | blk.11.attn_v.weight | 0xe64930c0 | 0x88000 |
| 142 | blk.11.ffn_down_exps.weight | 0xe651b0c0 | 0x8400000 |
| 143 | blk.11.ffn_gate_exps.weight | 0xee91b0c0 | 0x5280000 |
| 144 | blk.11.ffn_gate_inp.weight | 0xf3b9b0c0 | 0x100000 |
| 145 | blk.11.ffn_norm.weight | 0xf3c9b0c0 | 0x2000 |
| 146 | blk.11.ffn_up_exps.weight | 0xf3c9d0c0 | 0x5280000 |
| 147 | blk.12.attn_k.weight | 0xf8f1d0c0 | 0x6e000 |
| 148 | blk.12.attn_k_norm.weight | 0xf8f8b0c0 | 0x200 |
| 149 | blk.12.attn_norm.weight | 0xf8f8b2c0 | 0x2000 |
| 150 | blk.12.attn_output.weight | 0xf8f8d2c0 | 0x480000 |
| 151 | blk.12.attn_q.weight | 0xf940d2c0 | 0x370000 |
| 152 | blk.12.attn_q_norm.weight | 0xf977d2c0 | 0x200 |
| 153 | blk.12.attn_v.weight | 0xf977d4c0 | 0x88000 |
| 154 | blk.12.ffn_down_exps.weight | 0xf98054c0 | 0x8400000 |
| 155 | blk.12.ffn_gate_exps.weight | 0x101c054c0 | 0x5280000 |
| 156 | blk.12.ffn_gate_inp.weight | 0x106e854c0 | 0x100000 |
| 157 | blk.12.ffn_norm.weight | 0x106f854c0 | 0x2000 |
| 158 | blk.12.ffn_up_exps.weight | 0x106f874c0 | 0x5280000 |
| 159 | blk.13.attn_k.weight | 0x10c2074c0 | 0x6e000 |
| 160 | blk.13.attn_k_norm.weight | 0x10c2754c0 | 0x200 |
| 161 | blk.13.attn_norm.weight | 0x10c2756c0 | 0x2000 |
| 162 | blk.13.attn_output.weight | 0x10c2776c0 | 0x480000 |
| 163 | blk.13.attn_q.weight | 0x10c6f76c0 | 0x370000 |
| 164 | blk.13.attn_q_norm.weight | 0x10ca676c0 | 0x200 |
| 165 | blk.13.attn_v.weight | 0x10ca678c0 | 0x88000 |
| 166 | blk.13.ffn_down_exps.weight | 0x10caef8c0 | 0x8400000 |
| 167 | blk.13.ffn_gate_exps.weight | 0x114eef8c0 | 0x6c00000 |
| 168 | blk.13.ffn_gate_inp.weight | 0x11baef8c0 | 0x100000 |
| 169 | blk.13.ffn_norm.weight | 0x11bbef8c0 | 0x2000 |
| 170 | blk.13.ffn_up_exps.weight | 0x11bbf18c0 | 0x6c00000 |
| 171 | blk.14.attn_k.weight | 0x1227f18c0 | 0x6e000 |
| 172 | blk.14.attn_k_norm.weight | 0x12285f8c0 | 0x200 |
| 173 | blk.14.attn_norm.weight | 0x12285fac0 | 0x2000 |
| 174 | blk.14.attn_output.weight | 0x122861ac0 | 0x480000 |
| 175 | blk.14.attn_q.weight | 0x122ce1ac0 | 0x370000 |
| 176 | blk.14.attn_q_norm.weight | 0x123051ac0 | 0x200 |
| 177 | blk.14.attn_v.weight | 0x123051cc0 | 0x88000 |
| 178 | blk.14.ffn_down_exps.weight | 0x1230d9cc0 | 0x8400000 |
| 179 | blk.14.ffn_gate_exps.weight | 0x12b4d9cc0 | 0x5280000 |
| 180 | blk.14.ffn_gate_inp.weight | 0x130759cc0 | 0x100000 |
| 181 | blk.14.ffn_norm.weight | 0x130859cc0 | 0x2000 |
| 182 | blk.14.ffn_up_exps.weight | 0x13085bcc0 | 0x5280000 |
| 183 | blk.15.attn_k.weight | 0x135adbcc0 | 0x6e000 |
| 184 | blk.15.attn_k_norm.weight | 0x135b49cc0 | 0x200 |
| 185 | blk.15.attn_norm.weight | 0x135b49ec0 | 0x2000 |
| 186 | blk.15.attn_output.weight | 0x135b4bec0 | 0x480000 |
| 187 | blk.15.attn_q.weight | 0x135fcbec0 | 0x370000 |
| 188 | blk.15.attn_q_norm.weight | 0x13633bec0 | 0x200 |
| 189 | blk.15.attn_v.weight | 0x13633c0c0 | 0x88000 |
| 190 | blk.15.ffn_down_exps.weight | 0x1363c40c0 | 0x8400000 |
| 191 | blk.15.ffn_gate_exps.weight | 0x13e7c40c0 | 0x6c00000 |
| 192 | blk.15.ffn_gate_inp.weight | 0x1453c40c0 | 0x100000 |
| 193 | blk.15.ffn_norm.weight | 0x1454c40c0 | 0x2000 |
| 194 | blk.15.ffn_up_exps.weight | 0x1454c60c0 | 0x6c00000 |
| 195 | blk.16.attn_k.weight | 0x14c0c60c0 | 0x6e000 |
| 196 | blk.16.attn_k_norm.weight | 0x14c1340c0 | 0x200 |
| 197 | blk.16.attn_norm.weight | 0x14c1342c0 | 0x2000 |
| 198 | blk.16.attn_output.weight | 0x14c1362c0 | 0x480000 |
| 199 | blk.16.attn_q.weight | 0x14c5b62c0 | 0x370000 |
| 200 | blk.16.attn_q_norm.weight | 0x14c9262c0 | 0x200 |
| 201 | blk.16.attn_v.weight | 0x14c9264c0 | 0x88000 |
| 202 | blk.16.ffn_down_exps.weight | 0x14c9ae4c0 | 0x8400000 |
| 203 | blk.16.ffn_gate_exps.weight | 0x154dae4c0 | 0x5280000 |
| 204 | blk.16.ffn_gate_inp.weight | 0x15a02e4c0 | 0x100000 |
| 205 | blk.16.ffn_norm.weight | 0x15a12e4c0 | 0x2000 |
| 206 | blk.16.ffn_up_exps.weight | 0x15a1304c0 | 0x5280000 |
| 207 | blk.17.attn_k.weight | 0x15f3b04c0 | 0x6e000 |
| 208 | blk.17.attn_k_norm.weight | 0x15f41e4c0 | 0x200 |
| 209 | blk.17.attn_norm.weight | 0x15f41e6c0 | 0x2000 |
| 210 | blk.17.attn_output.weight | 0x15f4206c0 | 0x480000 |
| 211 | blk.17.attn_q.weight | 0x15f8a06c0 | 0x370000 |
| 212 | blk.17.attn_q_norm.weight | 0x15fc106c0 | 0x200 |
| 213 | blk.17.attn_v.weight | 0x15fc108c0 | 0x88000 |
| 214 | blk.17.ffn_down_exps.weight | 0x15fc988c0 | 0x8400000 |
| 215 | blk.17.ffn_gate_exps.weight | 0x1680988c0 | 0x5280000 |
| 216 | blk.17.ffn_gate_inp.weight | 0x16d3188c0 | 0x100000 |
| 217 | blk.17.ffn_norm.weight | 0x16d4188c0 | 0x2000 |
| 218 | blk.17.ffn_up_exps.weight | 0x16d41a8c0 | 0x5280000 |
| 219 | blk.18.attn_k.weight | 0x17269a8c0 | 0x6e000 |
| 220 | blk.18.attn_k_norm.weight | 0x1727088c0 | 0x200 |
| 221 | blk.18.attn_norm.weight | 0x172708ac0 | 0x2000 |
| 222 | blk.18.attn_output.weight | 0x17270aac0 | 0x480000 |
| 223 | blk.18.attn_q.weight | 0x172b8aac0 | 0x370000 |
| 224 | blk.18.attn_q_norm.weight | 0x172efaac0 | 0x200 |
| 225 | blk.18.attn_v.weight | 0x172efacc0 | 0x88000 |
| 226 | blk.18.ffn_down_exps.weight | 0x172f82cc0 | 0x8400000 |
| 227 | blk.18.ffn_gate_exps.weight | 0x17b382cc0 | 0x5280000 |
| 228 | blk.18.ffn_gate_inp.weight | 0x180602cc0 | 0x100000 |
| 229 | blk.18.ffn_norm.weight | 0x180702cc0 | 0x2000 |
| 230 | blk.18.ffn_up_exps.weight | 0x180704cc0 | 0x5280000 |
| 231 | blk.19.attn_k.weight | 0x185984cc0 | 0x6e000 |
| 232 | blk.19.attn_k_norm.weight | 0x1859f2cc0 | 0x200 |
| 233 | blk.19.attn_norm.weight | 0x1859f2ec0 | 0x2000 |
| 234 | blk.19.attn_output.weight | 0x1859f4ec0 | 0x480000 |
| 235 | blk.19.attn_q.weight | 0x185e74ec0 | 0x370000 |
| 236 | blk.19.attn_q_norm.weight | 0x1861e4ec0 | 0x200 |
| 237 | blk.19.attn_v.weight | 0x1861e50c0 | 0x88000 |
| 238 | blk.19.ffn_down_exps.weight | 0x18626d0c0 | 0x8400000 |
| 239 | blk.19.ffn_gate_exps.weight | 0x18e66d0c0 | 0x5280000 |
| 240 | blk.19.ffn_gate_inp.weight | 0x1938ed0c0 | 0x100000 |
| 241 | blk.19.ffn_norm.weight | 0x1939ed0c0 | 0x2000 |
| 242 | blk.19.ffn_up_exps.weight | 0x1939ef0c0 | 0x5280000 |
| 243 | blk.20.attn_k.weight | 0x198c6f0c0 | 0x6e000 |
| 244 | blk.20.attn_k_norm.weight | 0x198cdd0c0 | 0x200 |
| 245 | blk.20.attn_norm.weight | 0x198cdd2c0 | 0x2000 |
| 246 | blk.20.attn_output.weight | 0x198cdf2c0 | 0x480000 |
| 247 | blk.20.attn_q.weight | 0x19915f2c0 | 0x370000 |
| 248 | blk.20.attn_q_norm.weight | 0x1994cf2c0 | 0x200 |
| 249 | blk.20.attn_v.weight | 0x1994cf4c0 | 0x88000 |
| 250 | blk.20.ffn_down_exps.weight | 0x1995574c0 | 0x8400000 |
| 251 | blk.20.ffn_gate_exps.weight | 0x1a19574c0 | 0x5280000 |
| 252 | blk.20.ffn_gate_inp.weight | 0x1a6bd74c0 | 0x100000 |
| 253 | blk.20.ffn_norm.weight | 0x1a6cd74c0 | 0x2000 |
| 254 | blk.20.ffn_up_exps.weight | 0x1a6cd94c0 | 0x5280000 |
| 255 | blk.21.attn_k.weight | 0x1abf594c0 | 0x6e000 |
| 256 | blk.21.attn_k_norm.weight | 0x1abfc74c0 | 0x200 |
| 257 | blk.21.attn_norm.weight | 0x1abfc76c0 | 0x2000 |
| 258 | blk.21.attn_output.weight | 0x1abfc96c0 | 0x480000 |
| 259 | blk.21.attn_q.weight | 0x1ac4496c0 | 0x370000 |
| 260 | blk.21.attn_q_norm.weight | 0x1ac7b96c0 | 0x200 |
| 261 | blk.21.attn_v.weight | 0x1ac7b98c0 | 0x88000 |
| 262 | blk.21.ffn_down_exps.weight | 0x1ac8418c0 | 0x8400000 |
| 263 | blk.21.ffn_gate_exps.weight | 0x1b4c418c0 | 0x5280000 |
| 264 | blk.21.ffn_gate_inp.weight | 0x1b9ec18c0 | 0x100000 |
| 265 | blk.21.ffn_norm.weight | 0x1b9fc18c0 | 0x2000 |
| 266 | blk.21.ffn_up_exps.weight | 0x1b9fc38c0 | 0x5280000 |
| 267 | blk.22.attn_k.weight | 0x1bf2438c0 | 0x6e000 |
| 268 | blk.22.attn_k_norm.weight | 0x1bf2b18c0 | 0x200 |
| 269 | blk.22.attn_norm.weight | 0x1bf2b1ac0 | 0x2000 |
| 270 | blk.22.attn_output.weight | 0x1bf2b3ac0 | 0x480000 |
| 271 | blk.22.attn_q.weight | 0x1bf733ac0 | 0x370000 |
| 272 | blk.22.attn_q_norm.weight | 0x1bfaa3ac0 | 0x200 |
| 273 | blk.22.attn_v.weight | 0x1bfaa3cc0 | 0x88000 |
| 274 | blk.22.ffn_down_exps.weight | 0x1bfb2bcc0 | 0x8400000 |
| 275 | blk.22.ffn_gate_exps.weight | 0x1c7f2bcc0 | 0x5280000 |
| 276 | blk.22.ffn_gate_inp.weight | 0x1cd1abcc0 | 0x100000 |
| 277 | blk.22.ffn_norm.weight | 0x1cd2abcc0 | 0x2000 |
| 278 | blk.22.ffn_up_exps.weight | 0x1cd2adcc0 | 0x5280000 |
| 279 | blk.23.attn_k.weight | 0x1d252dcc0 | 0x6e000 |
| 280 | blk.23.attn_k_norm.weight | 0x1d259bcc0 | 0x200 |
| 281 | blk.23.attn_norm.weight | 0x1d259bec0 | 0x2000 |
| 282 | blk.23.attn_output.weight | 0x1d259dec0 | 0x480000 |
| 283 | blk.23.attn_q.weight | 0x1d2a1dec0 | 0x370000 |
| 284 | blk.23.attn_q_norm.weight | 0x1d2d8dec0 | 0x200 |
| 285 | blk.23.attn_v.weight | 0x1d2d8e0c0 | 0x88000 |
| 286 | blk.23.ffn_down_exps.weight | 0x1d2e160c0 | 0x8400000 |
| 287 | blk.23.ffn_gate_exps.weight | 0x1db2160c0 | 0x5280000 |
| 288 | blk.23.ffn_gate_inp.weight | 0x1e04960c0 | 0x100000 |
| 289 | blk.23.ffn_norm.weight | 0x1e05960c0 | 0x2000 |
| 290 | blk.23.ffn_up_exps.weight | 0x1e05980c0 | 0x5280000 |
| 291 | blk.24.attn_k.weight | 0x1e58180c0 | 0x90000 |
| 292 | blk.24.attn_k_norm.weight | 0x1e58a80c0 | 0x200 |
| 293 | blk.24.attn_norm.weight | 0x1e58a82c0 | 0x2000 |
| 294 | blk.24.attn_output.weight | 0x1e58aa2c0 | 0x480000 |
| 295 | blk.24.attn_q.weight | 0x1e5d2a2c0 | 0x480000 |
| 296 | blk.24.attn_q_norm.weight | 0x1e61aa2c0 | 0x200 |
| 297 | blk.24.attn_v.weight | 0x1e61aa4c0 | 0x90000 |
| 298 | blk.24.ffn_down_exps.weight | 0x1e623a4c0 | 0x8400000 |
| 299 | blk.24.ffn_gate_exps.weight | 0x1ee63a4c0 | 0x5280000 |
| 300 | blk.24.ffn_gate_inp.weight | 0x1f38ba4c0 | 0x100000 |
| 301 | blk.24.ffn_norm.weight | 0x1f39ba4c0 | 0x2000 |
| 302 | blk.24.ffn_up_exps.weight | 0x1f39bc4c0 | 0x5280000 |
| 303 | blk.25.attn_k.weight | 0x1f8c3c4c0 | 0x90000 |
| 304 | blk.25.attn_k_norm.weight | 0x1f8ccc4c0 | 0x200 |
| 305 | blk.25.attn_norm.weight | 0x1f8ccc6c0 | 0x2000 |
| 306 | blk.25.attn_output.weight | 0x1f8cce6c0 | 0x480000 |
| 307 | blk.25.attn_q.weight | 0x1f914e6c0 | 0x480000 |
| 308 | blk.25.attn_q_norm.weight | 0x1f95ce6c0 | 0x200 |
| 309 | blk.25.attn_v.weight | 0x1f95ce8c0 | 0x90000 |
| 310 | blk.25.ffn_down_exps.weight | 0x1f965e8c0 | 0x8400000 |
| 311 | blk.25.ffn_gate_exps.weight | 0x201a5e8c0 | 0x6c00000 |
| 312 | blk.25.ffn_gate_inp.weight | 0x20865e8c0 | 0x100000 |
| 313 | blk.25.ffn_norm.weight | 0x20875e8c0 | 0x2000 |
| 314 | blk.25.ffn_up_exps.weight | 0x2087608c0 | 0x6c00000 |
| 315 | blk.26.attn_k.weight | 0x20f3608c0 | 0x90000 |
| 316 | blk.26.attn_k_norm.weight | 0x20f3f08c0 | 0x200 |
| 317 | blk.26.attn_norm.weight | 0x20f3f0ac0 | 0x2000 |
| 318 | blk.26.attn_output.weight | 0x20f3f2ac0 | 0x480000 |
| 319 | blk.26.attn_q.weight | 0x20f872ac0 | 0x480000 |
| 320 | blk.26.attn_q_norm.weight | 0x20fcf2ac0 | 0x200 |
| 321 | blk.26.attn_v.weight | 0x20fcf2cc0 | 0x90000 |
| 322 | blk.26.ffn_down_exps.weight | 0x20fd82cc0 | 0x8400000 |
| 323 | blk.26.ffn_gate_exps.weight | 0x218182cc0 | 0x5280000 |
| 324 | blk.26.ffn_gate_inp.weight | 0x21d402cc0 | 0x100000 |
| 325 | blk.26.ffn_norm.weight | 0x21d502cc0 | 0x2000 |
| 326 | blk.26.ffn_up_exps.weight | 0x21d504cc0 | 0x5280000 |
| 327 | blk.27.attn_k.weight | 0x222784cc0 | 0x90000 |
| 328 | blk.27.attn_k_norm.weight | 0x222814cc0 | 0x200 |
| 329 | blk.27.attn_norm.weight | 0x222814ec0 | 0x2000 |
| 330 | blk.27.attn_output.weight | 0x222816ec0 | 0x480000 |
| 331 | blk.27.attn_q.weight | 0x222c96ec0 | 0x480000 |
| 332 | blk.27.attn_q_norm.weight | 0x223116ec0 | 0x200 |
| 333 | blk.27.attn_v.weight | 0x2231170c0 | 0x90000 |
| 334 | blk.27.ffn_down_exps.weight | 0x2231a70c0 | 0x8400000 |
| 335 | blk.27.ffn_gate_exps.weight | 0x22b5a70c0 | 0x6c00000 |
| 336 | blk.27.ffn_gate_inp.weight | 0x2321a70c0 | 0x100000 |
| 337 | blk.27.ffn_norm.weight | 0x2322a70c0 | 0x2000 |
| 338 | blk.27.ffn_up_exps.weight | 0x2322a90c0 | 0x6c00000 |
| 339 | blk.28.attn_k.weight | 0x238ea90c0 | 0x90000 |
| 340 | blk.28.attn_k_norm.weight | 0x238f390c0 | 0x200 |
| 341 | blk.28.attn_norm.weight | 0x238f392c0 | 0x2000 |
| 342 | blk.28.attn_output.weight | 0x238f3b2c0 | 0x480000 |
| 343 | blk.28.attn_q.weight | 0x2393bb2c0 | 0x480000 |
| 344 | blk.28.attn_q_norm.weight | 0x23983b2c0 | 0x200 |
| 345 | blk.28.attn_v.weight | 0x23983b4c0 | 0x90000 |
| 346 | blk.28.ffn_down_exps.weight | 0x2398cb4c0 | 0x8400000 |
| 347 | blk.28.ffn_gate_exps.weight | 0x241ccb4c0 | 0x6c00000 |
| 348 | blk.28.ffn_gate_inp.weight | 0x2488cb4c0 | 0x100000 |
| 349 | blk.28.ffn_norm.weight | 0x2489cb4c0 | 0x2000 |
| 350 | blk.28.ffn_up_exps.weight | 0x2489cd4c0 | 0x6c00000 |
| 351 | blk.29.attn_k.weight | 0x24f5cd4c0 | 0x90000 |
| 352 | blk.29.attn_k_norm.weight | 0x24f65d4c0 | 0x200 |
| 353 | blk.29.attn_norm.weight | 0x24f65d6c0 | 0x2000 |
| 354 | blk.29.attn_output.weight | 0x24f65f6c0 | 0x480000 |
| 355 | blk.29.attn_q.weight | 0x24fadf6c0 | 0x480000 |
| 356 | blk.29.attn_q_norm.weight | 0x24ff5f6c0 | 0x200 |
| 357 | blk.29.attn_v.weight | 0x24ff5f8c0 | 0x90000 |
| 358 | blk.29.ffn_down_exps.weight | 0x24ffef8c0 | 0x8400000 |
| 359 | blk.29.ffn_gate_exps.weight | 0x2583ef8c0 | 0x6c00000 |
| 360 | blk.29.ffn_gate_inp.weight | 0x25efef8c0 | 0x100000 |
| 361 | blk.29.ffn_norm.weight | 0x25f0ef8c0 | 0x2000 |
| 362 | blk.29.ffn_up_exps.weight | 0x25f0f18c0 | 0x6c00000 |
| 363 | blk.30.attn_k.weight | 0x265cf18c0 | 0x90000 |
| 364 | blk.30.attn_k_norm.weight | 0x265d818c0 | 0x200 |
| 365 | blk.30.attn_norm.weight | 0x265d81ac0 | 0x2000 |
| 366 | blk.30.attn_output.weight | 0x265d83ac0 | 0x480000 |
| 367 | blk.30.attn_q.weight | 0x266203ac0 | 0x480000 |
| 368 | blk.30.attn_q_norm.weight | 0x266683ac0 | 0x200 |
| 369 | blk.30.attn_v.weight | 0x266683cc0 | 0x90000 |
| 370 | blk.30.ffn_down_exps.weight | 0x266713cc0 | 0x8400000 |
| 371 | blk.30.ffn_gate_exps.weight | 0x26eb13cc0 | 0x6c00000 |
| 372 | blk.30.ffn_gate_inp.weight | 0x275713cc0 | 0x100000 |
| 373 | blk.30.ffn_norm.weight | 0x275813cc0 | 0x2000 |
| 374 | blk.30.ffn_up_exps.weight | 0x275815cc0 | 0x6c00000 |
| 375 | blk.31.attn_k.weight | 0x27c415cc0 | 0x90000 |
| 376 | blk.31.attn_k_norm.weight | 0x27c4a5cc0 | 0x200 |
| 377 | blk.31.attn_norm.weight | 0x27c4a5ec0 | 0x2000 |
| 378 | blk.31.attn_output.weight | 0x27c4a7ec0 | 0x480000 |
| 379 | blk.31.attn_q.weight | 0x27c927ec0 | 0x480000 |
| 380 | blk.31.attn_q_norm.weight | 0x27cda7ec0 | 0x200 |
| 381 | blk.31.attn_v.weight | 0x27cda80c0 | 0x90000 |
| 382 | blk.31.ffn_down_exps.weight | 0x27ce380c0 | 0x8400000 |
| 383 | blk.31.ffn_gate_exps.weight | 0x2852380c0 | 0x6c00000 |
| 384 | blk.31.ffn_gate_inp.weight | 0x28be380c0 | 0x100000 |
| 385 | blk.31.ffn_norm.weight | 0x28bf380c0 | 0x2000 |
| 386 | blk.31.ffn_up_exps.weight | 0x28bf3a0c0 | 0x6c00000 |
| 387 | blk.32.attn_k.weight | 0x292b3a0c0 | 0x90000 |
| 388 | blk.32.attn_k_norm.weight | 0x292bca0c0 | 0x200 |
| 389 | blk.32.attn_norm.weight | 0x292bca2c0 | 0x2000 |
| 390 | blk.32.attn_output.weight | 0x292bcc2c0 | 0x480000 |
| 391 | blk.32.attn_q.weight | 0x29304c2c0 | 0x480000 |
| 392 | blk.32.attn_q_norm.weight | 0x2934cc2c0 | 0x200 |
| 393 | blk.32.attn_v.weight | 0x2934cc4c0 | 0x90000 |
| 394 | blk.32.ffn_down_exps.weight | 0x29355c4c0 | 0x8400000 |
| 395 | blk.32.ffn_gate_exps.weight | 0x29b95c4c0 | 0x6c00000 |
| 396 | blk.32.ffn_gate_inp.weight | 0x2a255c4c0 | 0x100000 |
| 397 | blk.32.ffn_norm.weight | 0x2a265c4c0 | 0x2000 |
| 398 | blk.32.ffn_up_exps.weight | 0x2a265e4c0 | 0x6c00000 |
| 399 | blk.33.attn_k.weight | 0x2a925e4c0 | 0x90000 |
| 400 | blk.33.attn_k_norm.weight | 0x2a92ee4c0 | 0x200 |
| 401 | blk.33.attn_norm.weight | 0x2a92ee6c0 | 0x2000 |
| 402 | blk.33.attn_output.weight | 0x2a92f06c0 | 0x480000 |
| 403 | blk.33.attn_q.weight | 0x2a97706c0 | 0x480000 |
| 404 | blk.33.attn_q_norm.weight | 0x2a9bf06c0 | 0x200 |
| 405 | blk.33.attn_v.weight | 0x2a9bf08c0 | 0x90000 |
| 406 | blk.33.ffn_down_exps.weight | 0x2a9c808c0 | 0x8400000 |
| 407 | blk.33.ffn_gate_exps.weight | 0x2b20808c0 | 0x6c00000 |
| 408 | blk.33.ffn_gate_inp.weight | 0x2b8c808c0 | 0x100000 |
| 409 | blk.33.ffn_norm.weight | 0x2b8d808c0 | 0x2000 |
| 410 | blk.33.ffn_up_exps.weight | 0x2b8d828c0 | 0x6c00000 |
| 411 | blk.34.attn_k.weight | 0x2bf9828c0 | 0x90000 |
| 412 | blk.34.attn_k_norm.weight | 0x2bfa128c0 | 0x200 |
| 413 | blk.34.attn_norm.weight | 0x2bfa12ac0 | 0x2000 |
| 414 | blk.34.attn_output.weight | 0x2bfa14ac0 | 0x480000 |
| 415 | blk.34.attn_q.weight | 0x2bfe94ac0 | 0x480000 |
| 416 | blk.34.attn_q_norm.weight | 0x2c0314ac0 | 0x200 |
| 417 | blk.34.attn_v.weight | 0x2c0314cc0 | 0x90000 |
| 418 | blk.34.ffn_down_exps.weight | 0x2c03a4cc0 | 0x8400000 |
| 419 | blk.34.ffn_gate_exps.weight | 0x2c87a4cc0 | 0x6c00000 |
| 420 | blk.34.ffn_gate_inp.weight | 0x2cf3a4cc0 | 0x100000 |
| 421 | blk.34.ffn_norm.weight | 0x2cf4a4cc0 | 0x2000 |
| 422 | blk.34.ffn_up_exps.weight | 0x2cf4a6cc0 | 0x6c00000 |
| 423 | blk.35.attn_k.weight | 0x2d60a6cc0 | 0x90000 |
| 424 | blk.35.attn_k_norm.weight | 0x2d6136cc0 | 0x200 |
| 425 | blk.35.attn_norm.weight | 0x2d6136ec0 | 0x2000 |
| 426 | blk.35.attn_output.weight | 0x2d6138ec0 | 0x480000 |
| 427 | blk.35.attn_q.weight | 0x2d65b8ec0 | 0x480000 |
| 428 | blk.35.attn_q_norm.weight | 0x2d6a38ec0 | 0x200 |
| 429 | blk.35.attn_v.weight | 0x2d6a390c0 | 0x90000 |
| 430 | blk.35.ffn_down_exps.weight | 0x2d6ac90c0 | 0x8400000 |
| 431 | blk.35.ffn_gate_exps.weight | 0x2deec90c0 | 0x6c00000 |
| 432 | blk.35.ffn_gate_inp.weight | 0x2e5ac90c0 | 0x100000 |
| 433 | blk.35.ffn_norm.weight | 0x2e5bc90c0 | 0x2000 |
| 434 | blk.35.ffn_up_exps.weight | 0x2e5bcb0c0 | 0x6c00000 |
| 435 | blk.36.attn_k.weight | 0x2ec7cb0c0 | 0x90000 |
| 436 | blk.36.attn_k_norm.weight | 0x2ec85b0c0 | 0x200 |
| 437 | blk.36.attn_norm.weight | 0x2ec85b2c0 | 0x2000 |
| 438 | blk.36.attn_output.weight | 0x2ec85d2c0 | 0x480000 |
| 439 | blk.36.attn_q.weight | 0x2eccdd2c0 | 0x480000 |
| 440 | blk.36.attn_q_norm.weight | 0x2ed15d2c0 | 0x200 |
| 441 | blk.36.attn_v.weight | 0x2ed15d4c0 | 0x90000 |
| 442 | blk.36.ffn_down_exps.weight | 0x2ed1ed4c0 | 0x8400000 |
| 443 | blk.36.ffn_gate_exps.weight | 0x2f55ed4c0 | 0x6c00000 |
| 444 | blk.36.ffn_gate_inp.weight | 0x2fc1ed4c0 | 0x100000 |
| 445 | blk.36.ffn_norm.weight | 0x2fc2ed4c0 | 0x2000 |
| 446 | blk.36.ffn_up_exps.weight | 0x2fc2ef4c0 | 0x6c00000 |
| 447 | blk.37.attn_k.weight | 0x302eef4c0 | 0x90000 |
| 448 | blk.37.attn_k_norm.weight | 0x302f7f4c0 | 0x200 |
| 449 | blk.37.attn_norm.weight | 0x302f7f6c0 | 0x2000 |
| 450 | blk.37.attn_output.weight | 0x302f816c0 | 0x480000 |
| 451 | blk.37.attn_q.weight | 0x3034016c0 | 0x480000 |
| 452 | blk.37.attn_q_norm.weight | 0x3038816c0 | 0x200 |
| 453 | blk.37.attn_v.weight | 0x3038818c0 | 0x90000 |
| 454 | blk.37.ffn_down_exps.weight | 0x3039118c0 | 0x8400000 |
| 455 | blk.37.ffn_gate_exps.weight | 0x30bd118c0 | 0x6c00000 |
| 456 | blk.37.ffn_gate_inp.weight | 0x3129118c0 | 0x100000 |
| 457 | blk.37.ffn_norm.weight | 0x312a118c0 | 0x2000 |
| 458 | blk.37.ffn_up_exps.weight | 0x312a138c0 | 0x6c00000 |
| 459 | blk.38.attn_k.weight | 0x3196138c0 | 0x90000 |
| 460 | blk.38.attn_k_norm.weight | 0x3196a38c0 | 0x200 |
| 461 | blk.38.attn_norm.weight | 0x3196a3ac0 | 0x2000 |
| 462 | blk.38.attn_output.weight | 0x3196a5ac0 | 0x480000 |
| 463 | blk.38.attn_q.weight | 0x319b25ac0 | 0x480000 |
| 464 | blk.38.attn_q_norm.weight | 0x319fa5ac0 | 0x200 |
| 465 | blk.38.attn_v.weight | 0x319fa5cc0 | 0x90000 |
| 466 | blk.38.ffn_down_exps.weight | 0x31a035cc0 | 0x8400000 |
| 467 | blk.38.ffn_gate_exps.weight | 0x322435cc0 | 0x6c00000 |
| 468 | blk.38.ffn_gate_inp.weight | 0x329035cc0 | 0x100000 |
| 469 | blk.38.ffn_norm.weight | 0x329135cc0 | 0x2000 |
| 470 | blk.38.ffn_up_exps.weight | 0x329137cc0 | 0x6c00000 |
| 471 | blk.39.attn_k.weight | 0x32fd37cc0 | 0x90000 |
| 472 | blk.39.attn_k_norm.weight | 0x32fdc7cc0 | 0x200 |
| 473 | blk.39.attn_norm.weight | 0x32fdc7ec0 | 0x2000 |
| 474 | blk.39.attn_output.weight | 0x32fdc9ec0 | 0x480000 |
| 475 | blk.39.attn_q.weight | 0x330249ec0 | 0x480000 |
| 476 | blk.39.attn_q_norm.weight | 0x3306c9ec0 | 0x200 |
| 477 | blk.39.attn_v.weight | 0x3306ca0c0 | 0x90000 |
| 478 | blk.39.ffn_down_exps.weight | 0x33075a0c0 | 0x8400000 |
| 479 | blk.39.ffn_gate_exps.weight | 0x338b5a0c0 | 0x6c00000 |
| 480 | blk.39.ffn_gate_inp.weight | 0x33f75a0c0 | 0x100000 |
| 481 | blk.39.ffn_norm.weight | 0x33f85a0c0 | 0x2000 |
| 482 | blk.39.ffn_up_exps.weight | 0x33f85c0c0 | 0x6c00000 |
| 483 | blk.40.attn_k.weight | 0x34645c0c0 | 0x90000 |
| 484 | blk.40.attn_k_norm.weight | 0x3464ec0c0 | 0x200 |
| 485 | blk.40.attn_norm.weight | 0x3464ec2c0 | 0x2000 |
| 486 | blk.40.attn_output.weight | 0x3464ee2c0 | 0x480000 |
| 487 | blk.40.attn_q.weight | 0x34696e2c0 | 0x480000 |
| 488 | blk.40.attn_q_norm.weight | 0x346dee2c0 | 0x200 |
| 489 | blk.40.attn_v.weight | 0x346dee4c0 | 0x90000 |
| 490 | blk.40.ffn_down_exps.weight | 0x346e7e4c0 | 0x8400000 |
| 491 | blk.40.ffn_gate_exps.weight | 0x34f27e4c0 | 0x6c00000 |
| 492 | blk.40.ffn_gate_inp.weight | 0x355e7e4c0 | 0x100000 |
| 493 | blk.40.ffn_norm.weight | 0x355f7e4c0 | 0x2000 |
| 494 | blk.40.ffn_up_exps.weight | 0x355f804c0 | 0x6c00000 |
| 495 | blk.41.attn_k.weight | 0x35cb804c0 | 0x90000 |
| 496 | blk.41.attn_k_norm.weight | 0x35cc104c0 | 0x200 |
| 497 | blk.41.attn_norm.weight | 0x35cc106c0 | 0x2000 |
| 498 | blk.41.attn_output.weight | 0x35cc126c0 | 0x480000 |
| 499 | blk.41.attn_q.weight | 0x35d0926c0 | 0x480000 |
| 500 | blk.41.attn_q_norm.weight | 0x35d5126c0 | 0x200 |
| 501 | blk.41.attn_v.weight | 0x35d5128c0 | 0x90000 |
| 502 | blk.41.ffn_down_exps.weight | 0x35d5a28c0 | 0x8400000 |
| 503 | blk.41.ffn_gate_exps.weight | 0x3659a28c0 | 0x6c00000 |
| 504 | blk.41.ffn_gate_inp.weight | 0x36c5a28c0 | 0x100000 |
| 505 | blk.41.ffn_norm.weight | 0x36c6a28c0 | 0x2000 |
| 506 | blk.41.ffn_up_exps.weight | 0x36c6a48c0 | 0x6c00000 |
| 507 | blk.42.attn_k.weight | 0x3732a48c0 | 0x90000 |
| 508 | blk.42.attn_k_norm.weight | 0x3733348c0 | 0x200 |
| 509 | blk.42.attn_norm.weight | 0x373334ac0 | 0x2000 |
| 510 | blk.42.attn_output.weight | 0x373336ac0 | 0x480000 |
| 511 | blk.42.attn_q.weight | 0x3737b6ac0 | 0x480000 |
| 512 | blk.42.attn_q_norm.weight | 0x373c36ac0 | 0x200 |
| 513 | blk.42.attn_v.weight | 0x373c36cc0 | 0x90000 |
| 514 | blk.42.ffn_down_exps.weight | 0x373cc6cc0 | 0x8400000 |
| 515 | blk.42.ffn_gate_exps.weight | 0x37c0c6cc0 | 0x6c00000 |
| 516 | blk.42.ffn_gate_inp.weight | 0x382cc6cc0 | 0x100000 |
| 517 | blk.42.ffn_norm.weight | 0x382dc6cc0 | 0x2000 |
| 518 | blk.42.ffn_up_exps.weight | 0x382dc8cc0 | 0x6c00000 |
| 519 | blk.43.attn_k.weight | 0x3899c8cc0 | 0x90000 |
| 520 | blk.43.attn_k_norm.weight | 0x389a58cc0 | 0x200 |
| 521 | blk.43.attn_norm.weight | 0x389a58ec0 | 0x2000 |
| 522 | blk.43.attn_output.weight | 0x389a5aec0 | 0x480000 |
| 523 | blk.43.attn_q.weight | 0x389edaec0 | 0x480000 |
| 524 | blk.43.attn_q_norm.weight | 0x38a35aec0 | 0x200 |
| 525 | blk.43.attn_v.weight | 0x38a35b0c0 | 0x90000 |
| 526 | blk.43.ffn_down_exps.weight | 0x38a3eb0c0 | 0x8400000 |
| 527 | blk.43.ffn_gate_exps.weight | 0x3927eb0c0 | 0x6c00000 |
| 528 | blk.43.ffn_gate_inp.weight | 0x3993eb0c0 | 0x100000 |
| 529 | blk.43.ffn_norm.weight | 0x3994eb0c0 | 0x2000 |
| 530 | blk.43.ffn_up_exps.weight | 0x3994ed0c0 | 0x6c00000 |
| 531 | blk.44.attn_k.weight | 0x3a00ed0c0 | 0x90000 |
| 532 | blk.44.attn_k_norm.weight | 0x3a017d0c0 | 0x200 |
| 533 | blk.44.attn_norm.weight | 0x3a017d2c0 | 0x2000 |
| 534 | blk.44.attn_output.weight | 0x3a017f2c0 | 0x480000 |
| 535 | blk.44.attn_q.weight | 0x3a05ff2c0 | 0x480000 |
| 536 | blk.44.attn_q_norm.weight | 0x3a0a7f2c0 | 0x200 |
| 537 | blk.44.attn_v.weight | 0x3a0a7f4c0 | 0x90000 |
| 538 | blk.44.ffn_down_exps.weight | 0x3a0b0f4c0 | 0x8400000 |
| 539 | blk.44.ffn_gate_exps.weight | 0x3a8f0f4c0 | 0x6c00000 |
| 540 | blk.44.ffn_gate_inp.weight | 0x3afb0f4c0 | 0x100000 |
| 541 | blk.44.ffn_norm.weight | 0x3afc0f4c0 | 0x2000 |
| 542 | blk.44.ffn_up_exps.weight | 0x3afc114c0 | 0x6c00000 |
| 543 | blk.45.attn_k.weight | 0x3b68114c0 | 0x90000 |
| 544 | blk.45.attn_k_norm.weight | 0x3b68a14c0 | 0x200 |
| 545 | blk.45.attn_norm.weight | 0x3b68a16c0 | 0x2000 |
| 546 | blk.45.attn_output.weight | 0x3b68a36c0 | 0x480000 |
| 547 | blk.45.attn_q.weight | 0x3b6d236c0 | 0x480000 |
| 548 | blk.45.attn_q_norm.weight | 0x3b71a36c0 | 0x200 |
| 549 | blk.45.attn_v.weight | 0x3b71a38c0 | 0x90000 |
| 550 | blk.45.ffn_down_exps.weight | 0x3b72338c0 | 0x8400000 |
| 551 | blk.45.ffn_gate_exps.weight | 0x3bf6338c0 | 0x6c00000 |
| 552 | blk.45.ffn_gate_inp.weight | 0x3c62338c0 | 0x100000 |
| 553 | blk.45.ffn_norm.weight | 0x3c63338c0 | 0x2000 |
| 554 | blk.45.ffn_up_exps.weight | 0x3c63358c0 | 0x6c00000 |
| 555 | blk.46.attn_k.weight | 0x3ccf358c0 | 0x90000 |
| 556 | blk.46.attn_k_norm.weight | 0x3ccfc58c0 | 0x200 |
| 557 | blk.46.attn_norm.weight | 0x3ccfc5ac0 | 0x2000 |
| 558 | blk.46.attn_output.weight | 0x3ccfc7ac0 | 0x480000 |
| 559 | blk.46.attn_q.weight | 0x3cd447ac0 | 0x480000 |
| 560 | blk.46.attn_q_norm.weight | 0x3cd8c7ac0 | 0x200 |
| 561 | blk.46.attn_v.weight | 0x3cd8c7cc0 | 0x90000 |
| 562 | blk.46.ffn_down_exps.weight | 0x3cd957cc0 | 0x8400000 |
| 563 | blk.46.ffn_gate_exps.weight | 0x3d5d57cc0 | 0x6c00000 |
| 564 | blk.46.ffn_gate_inp.weight | 0x3dc957cc0 | 0x100000 |
| 565 | blk.46.ffn_norm.weight | 0x3dca57cc0 | 0x2000 |
| 566 | blk.46.ffn_up_exps.weight | 0x3dca59cc0 | 0x6c00000 |
| 567 | blk.47.attn_k.weight | 0x3e3659cc0 | 0x90000 |
| 568 | blk.47.attn_k_norm.weight | 0x3e36e9cc0 | 0x200 |
| 569 | blk.47.attn_norm.weight | 0x3e36e9ec0 | 0x2000 |
| 570 | blk.47.attn_output.weight | 0x3e36ebec0 | 0x480000 |
| 571 | blk.47.attn_q.weight | 0x3e3b6bec0 | 0x480000 |
| 572 | blk.47.attn_q_norm.weight | 0x3e3febec0 | 0x200 |
| 573 | blk.47.attn_v.weight | 0x3e3fec0c0 | 0x90000 |
| 574 | blk.47.ffn_down_exps.weight | 0x3e407c0c0 | 0x8400000 |
| 575 | blk.47.ffn_gate_exps.weight | 0x3ec47c0c0 | 0x6c00000 |
| 576 | blk.47.ffn_gate_inp.weight | 0x3f307c0c0 | 0x100000 |
| 577 | blk.47.ffn_norm.weight | 0x3f317c0c0 | 0x2000 |
| 578 | blk.47.ffn_up_exps.weight | 0x3f317e0c0 | 0x6c00000 |
### <a name="base">Base Tensor Group : ~622M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:-------------------|:---------------------------------|:------------------|:----------------------|:-------|
| 0 | output.weight | Output (W) | (~311M) 311164928 | 2048 x 151936 x 1 x 1 | IQ4_NL |
| 1 | output_norm.weight | Output Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 2 | token_embd.weight | Token Embedding (W) | (~311M) 311164928 | 2048 x 151936 x 1 x 1 | IQ3_S |
- Total elements in base: (~622M) 622331904
- Percentage of total elements: 2.04%
### <a name="blk_0">Block 0 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 3 | blk.0.attn_k.weight | Block 0 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 4 | blk.0.attn_k_norm.weight | Block 0 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 5 | blk.0.attn_norm.weight | Block 0 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 6 | blk.0.attn_output.weight | Block 0 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 7 | blk.0.attn_q.weight | Block 0 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 8 | blk.0.attn_q_norm.weight | Block 0 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 9 | blk.0.attn_v.weight | Block 0 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 10 | blk.0.ffn_down_exps.weight | Block 0 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 11 | blk.0.ffn_gate_exps.weight | Block 0 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 12 | blk.0.ffn_gate_inp.weight | Block 0 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 13 | blk.0.ffn_norm.weight | Block 0 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 14 | blk.0.ffn_up_exps.weight | Block 0 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.0: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_1">Block 1 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 15 | blk.1.attn_k.weight | Block 1 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 16 | blk.1.attn_k_norm.weight | Block 1 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 17 | blk.1.attn_norm.weight | Block 1 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 18 | blk.1.attn_output.weight | Block 1 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 19 | blk.1.attn_q.weight | Block 1 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 20 | blk.1.attn_q_norm.weight | Block 1 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 21 | blk.1.attn_v.weight | Block 1 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 22 | blk.1.ffn_down_exps.weight | Block 1 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 23 | blk.1.ffn_gate_exps.weight | Block 1 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 24 | blk.1.ffn_gate_inp.weight | Block 1 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 25 | blk.1.ffn_norm.weight | Block 1 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 26 | blk.1.ffn_up_exps.weight | Block 1 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.1: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_2">Block 2 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 27 | blk.2.attn_k.weight | Block 2 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 28 | blk.2.attn_k_norm.weight | Block 2 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 29 | blk.2.attn_norm.weight | Block 2 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 30 | blk.2.attn_output.weight | Block 2 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 31 | blk.2.attn_q.weight | Block 2 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 32 | blk.2.attn_q_norm.weight | Block 2 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 33 | blk.2.attn_v.weight | Block 2 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 34 | blk.2.ffn_down_exps.weight | Block 2 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 35 | blk.2.ffn_gate_exps.weight | Block 2 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 36 | blk.2.ffn_gate_inp.weight | Block 2 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 37 | blk.2.ffn_norm.weight | Block 2 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 38 | blk.2.ffn_up_exps.weight | Block 2 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.2: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_3">Block 3 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 39 | blk.3.attn_k.weight | Block 3 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 40 | blk.3.attn_k_norm.weight | Block 3 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 41 | blk.3.attn_norm.weight | Block 3 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 42 | blk.3.attn_output.weight | Block 3 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 43 | blk.3.attn_q.weight | Block 3 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 44 | blk.3.attn_q_norm.weight | Block 3 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 45 | blk.3.attn_v.weight | Block 3 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 46 | blk.3.ffn_down_exps.weight | Block 3 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 47 | blk.3.ffn_gate_exps.weight | Block 3 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 48 | blk.3.ffn_gate_inp.weight | Block 3 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 49 | blk.3.ffn_norm.weight | Block 3 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 50 | blk.3.ffn_up_exps.weight | Block 3 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.3: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_4">Block 4 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 51 | blk.4.attn_k.weight | Block 4 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 52 | blk.4.attn_k_norm.weight | Block 4 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 53 | blk.4.attn_norm.weight | Block 4 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 54 | blk.4.attn_output.weight | Block 4 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 55 | blk.4.attn_q.weight | Block 4 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 56 | blk.4.attn_q_norm.weight | Block 4 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 57 | blk.4.attn_v.weight | Block 4 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 58 | blk.4.ffn_down_exps.weight | Block 4 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 59 | blk.4.ffn_gate_exps.weight | Block 4 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 60 | blk.4.ffn_gate_inp.weight | Block 4 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 61 | blk.4.ffn_norm.weight | Block 4 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 62 | blk.4.ffn_up_exps.weight | Block 4 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.4: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_5">Block 5 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 63 | blk.5.attn_k.weight | Block 5 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 64 | blk.5.attn_k_norm.weight | Block 5 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 65 | blk.5.attn_norm.weight | Block 5 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 66 | blk.5.attn_output.weight | Block 5 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 67 | blk.5.attn_q.weight | Block 5 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 68 | blk.5.attn_q_norm.weight | Block 5 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 69 | blk.5.attn_v.weight | Block 5 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 70 | blk.5.ffn_down_exps.weight | Block 5 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 71 | blk.5.ffn_gate_exps.weight | Block 5 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 72 | blk.5.ffn_gate_inp.weight | Block 5 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 73 | blk.5.ffn_norm.weight | Block 5 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 74 | blk.5.ffn_up_exps.weight | Block 5 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.5: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_6">Block 6 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 75 | blk.6.attn_k.weight | Block 6 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 76 | blk.6.attn_k_norm.weight | Block 6 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 77 | blk.6.attn_norm.weight | Block 6 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 78 | blk.6.attn_output.weight | Block 6 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 79 | blk.6.attn_q.weight | Block 6 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 80 | blk.6.attn_q_norm.weight | Block 6 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 81 | blk.6.attn_v.weight | Block 6 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 82 | blk.6.ffn_down_exps.weight | Block 6 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 83 | blk.6.ffn_gate_exps.weight | Block 6 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 84 | blk.6.ffn_gate_inp.weight | Block 6 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 85 | blk.6.ffn_norm.weight | Block 6 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 86 | blk.6.ffn_up_exps.weight | Block 6 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.6: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_7">Block 7 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 87 | blk.7.attn_k.weight | Block 7 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 88 | blk.7.attn_k_norm.weight | Block 7 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 89 | blk.7.attn_norm.weight | Block 7 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 90 | blk.7.attn_output.weight | Block 7 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 91 | blk.7.attn_q.weight | Block 7 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 92 | blk.7.attn_q_norm.weight | Block 7 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 93 | blk.7.attn_v.weight | Block 7 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 94 | blk.7.ffn_down_exps.weight | Block 7 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 95 | blk.7.ffn_gate_exps.weight | Block 7 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 96 | blk.7.ffn_gate_inp.weight | Block 7 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 97 | blk.7.ffn_norm.weight | Block 7 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 98 | blk.7.ffn_up_exps.weight | Block 7 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.7: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_8">Block 8 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 99 | blk.8.attn_k.weight | Block 8 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 100 | blk.8.attn_k_norm.weight | Block 8 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 101 | blk.8.attn_norm.weight | Block 8 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 102 | blk.8.attn_output.weight | Block 8 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 103 | blk.8.attn_q.weight | Block 8 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 104 | blk.8.attn_q_norm.weight | Block 8 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 105 | blk.8.attn_v.weight | Block 8 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 106 | blk.8.ffn_down_exps.weight | Block 8 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 107 | blk.8.ffn_gate_exps.weight | Block 8 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 108 | blk.8.ffn_gate_inp.weight | Block 8 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 109 | blk.8.ffn_norm.weight | Block 8 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 110 | blk.8.ffn_up_exps.weight | Block 8 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.8: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_9">Block 9 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:---------------------------|:------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 111 | blk.9.attn_k.weight | Block 9 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 112 | blk.9.attn_k_norm.weight | Block 9 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 113 | blk.9.attn_norm.weight | Block 9 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 114 | blk.9.attn_output.weight | Block 9 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 115 | blk.9.attn_q.weight | Block 9 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 116 | blk.9.attn_q_norm.weight | Block 9 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 117 | blk.9.attn_v.weight | Block 9 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 118 | blk.9.ffn_down_exps.weight | Block 9 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 119 | blk.9.ffn_gate_exps.weight | Block 9 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 120 | blk.9.ffn_gate_inp.weight | Block 9 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 121 | blk.9.ffn_norm.weight | Block 9 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 122 | blk.9.ffn_up_exps.weight | Block 9 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.9: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_10">Block 10 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 123 | blk.10.attn_k.weight | Block 10 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 124 | blk.10.attn_k_norm.weight | Block 10 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 125 | blk.10.attn_norm.weight | Block 10 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 126 | blk.10.attn_output.weight | Block 10 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 127 | blk.10.attn_q.weight | Block 10 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 128 | blk.10.attn_q_norm.weight | Block 10 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 129 | blk.10.attn_v.weight | Block 10 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 130 | blk.10.ffn_down_exps.weight | Block 10 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 131 | blk.10.ffn_gate_exps.weight | Block 10 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 132 | blk.10.ffn_gate_inp.weight | Block 10 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 133 | blk.10.ffn_norm.weight | Block 10 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 134 | blk.10.ffn_up_exps.weight | Block 10 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.10: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_11">Block 11 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 135 | blk.11.attn_k.weight | Block 11 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 136 | blk.11.attn_k_norm.weight | Block 11 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 137 | blk.11.attn_norm.weight | Block 11 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 138 | blk.11.attn_output.weight | Block 11 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 139 | blk.11.attn_q.weight | Block 11 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 140 | blk.11.attn_q_norm.weight | Block 11 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 141 | blk.11.attn_v.weight | Block 11 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 142 | blk.11.ffn_down_exps.weight | Block 11 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 143 | blk.11.ffn_gate_exps.weight | Block 11 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 144 | blk.11.ffn_gate_inp.weight | Block 11 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 145 | blk.11.ffn_norm.weight | Block 11 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 146 | blk.11.ffn_up_exps.weight | Block 11 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.11: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_12">Block 12 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 147 | blk.12.attn_k.weight | Block 12 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 148 | blk.12.attn_k_norm.weight | Block 12 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 149 | blk.12.attn_norm.weight | Block 12 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 150 | blk.12.attn_output.weight | Block 12 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 151 | blk.12.attn_q.weight | Block 12 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 152 | blk.12.attn_q_norm.weight | Block 12 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 153 | blk.12.attn_v.weight | Block 12 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 154 | blk.12.ffn_down_exps.weight | Block 12 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 155 | blk.12.ffn_gate_exps.weight | Block 12 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 156 | blk.12.ffn_gate_inp.weight | Block 12 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 157 | blk.12.ffn_norm.weight | Block 12 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 158 | blk.12.ffn_up_exps.weight | Block 12 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.12: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_13">Block 13 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 159 | blk.13.attn_k.weight | Block 13 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 160 | blk.13.attn_k_norm.weight | Block 13 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 161 | blk.13.attn_norm.weight | Block 13 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 162 | blk.13.attn_output.weight | Block 13 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 163 | blk.13.attn_q.weight | Block 13 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 164 | blk.13.attn_q_norm.weight | Block 13 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 165 | blk.13.attn_v.weight | Block 13 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 166 | blk.13.ffn_down_exps.weight | Block 13 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 167 | blk.13.ffn_gate_exps.weight | Block 13 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 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 | IQ4_NL |
- Total elements in blk.13: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_14">Block 14 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 171 | blk.14.attn_k.weight | Block 14 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 172 | blk.14.attn_k_norm.weight | Block 14 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 173 | blk.14.attn_norm.weight | Block 14 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 174 | blk.14.attn_output.weight | Block 14 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 175 | blk.14.attn_q.weight | Block 14 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 176 | blk.14.attn_q_norm.weight | Block 14 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 177 | blk.14.attn_v.weight | Block 14 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 178 | blk.14.ffn_down_exps.weight | Block 14 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 179 | blk.14.ffn_gate_exps.weight | Block 14 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 180 | blk.14.ffn_gate_inp.weight | Block 14 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 181 | blk.14.ffn_norm.weight | Block 14 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 182 | blk.14.ffn_up_exps.weight | Block 14 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.14: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_15">Block 15 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 183 | blk.15.attn_k.weight | Block 15 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 184 | blk.15.attn_k_norm.weight | Block 15 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 185 | blk.15.attn_norm.weight | Block 15 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 186 | blk.15.attn_output.weight | Block 15 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 187 | blk.15.attn_q.weight | Block 15 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 188 | blk.15.attn_q_norm.weight | Block 15 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 189 | blk.15.attn_v.weight | Block 15 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 190 | blk.15.ffn_down_exps.weight | Block 15 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 191 | blk.15.ffn_gate_exps.weight | Block 15 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 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 | IQ4_NL |
- Total elements in blk.15: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_16">Block 16 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 195 | blk.16.attn_k.weight | Block 16 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 196 | blk.16.attn_k_norm.weight | Block 16 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 197 | blk.16.attn_norm.weight | Block 16 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 198 | blk.16.attn_output.weight | Block 16 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 199 | blk.16.attn_q.weight | Block 16 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 200 | blk.16.attn_q_norm.weight | Block 16 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 201 | blk.16.attn_v.weight | Block 16 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 202 | blk.16.ffn_down_exps.weight | Block 16 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 203 | blk.16.ffn_gate_exps.weight | Block 16 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 204 | blk.16.ffn_gate_inp.weight | Block 16 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 205 | blk.16.ffn_norm.weight | Block 16 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 206 | blk.16.ffn_up_exps.weight | Block 16 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.16: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_17">Block 17 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 207 | blk.17.attn_k.weight | Block 17 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 208 | blk.17.attn_k_norm.weight | Block 17 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 209 | blk.17.attn_norm.weight | Block 17 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 210 | blk.17.attn_output.weight | Block 17 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 211 | blk.17.attn_q.weight | Block 17 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 212 | blk.17.attn_q_norm.weight | Block 17 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 213 | blk.17.attn_v.weight | Block 17 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 214 | blk.17.ffn_down_exps.weight | Block 17 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 215 | blk.17.ffn_gate_exps.weight | Block 17 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 216 | blk.17.ffn_gate_inp.weight | Block 17 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 217 | blk.17.ffn_norm.weight | Block 17 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 218 | blk.17.ffn_up_exps.weight | Block 17 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.17: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_18">Block 18 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 219 | blk.18.attn_k.weight | Block 18 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 220 | blk.18.attn_k_norm.weight | Block 18 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 221 | blk.18.attn_norm.weight | Block 18 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 222 | blk.18.attn_output.weight | Block 18 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 223 | blk.18.attn_q.weight | Block 18 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 224 | blk.18.attn_q_norm.weight | Block 18 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 225 | blk.18.attn_v.weight | Block 18 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 226 | blk.18.ffn_down_exps.weight | Block 18 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 227 | blk.18.ffn_gate_exps.weight | Block 18 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 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 | IQ3_S |
- Total elements in blk.18: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_19">Block 19 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 231 | blk.19.attn_k.weight | Block 19 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 232 | blk.19.attn_k_norm.weight | Block 19 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 233 | blk.19.attn_norm.weight | Block 19 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 234 | blk.19.attn_output.weight | Block 19 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 235 | blk.19.attn_q.weight | Block 19 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 236 | blk.19.attn_q_norm.weight | Block 19 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 237 | blk.19.attn_v.weight | Block 19 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 238 | blk.19.ffn_down_exps.weight | Block 19 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 239 | blk.19.ffn_gate_exps.weight | Block 19 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 240 | blk.19.ffn_gate_inp.weight | Block 19 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 241 | blk.19.ffn_norm.weight | Block 19 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 242 | blk.19.ffn_up_exps.weight | Block 19 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.19: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_20">Block 20 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 243 | blk.20.attn_k.weight | Block 20 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 244 | blk.20.attn_k_norm.weight | Block 20 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 245 | blk.20.attn_norm.weight | Block 20 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 246 | blk.20.attn_output.weight | Block 20 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 247 | blk.20.attn_q.weight | Block 20 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 248 | blk.20.attn_q_norm.weight | Block 20 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 249 | blk.20.attn_v.weight | Block 20 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 250 | blk.20.ffn_down_exps.weight | Block 20 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 251 | blk.20.ffn_gate_exps.weight | Block 20 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 252 | blk.20.ffn_gate_inp.weight | Block 20 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 253 | blk.20.ffn_norm.weight | Block 20 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 254 | blk.20.ffn_up_exps.weight | Block 20 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.20: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_21">Block 21 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 255 | blk.21.attn_k.weight | Block 21 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 256 | blk.21.attn_k_norm.weight | Block 21 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 257 | blk.21.attn_norm.weight | Block 21 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 258 | blk.21.attn_output.weight | Block 21 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 259 | blk.21.attn_q.weight | Block 21 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 260 | blk.21.attn_q_norm.weight | Block 21 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 261 | blk.21.attn_v.weight | Block 21 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 262 | blk.21.ffn_down_exps.weight | Block 21 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 263 | blk.21.ffn_gate_exps.weight | Block 21 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 264 | blk.21.ffn_gate_inp.weight | Block 21 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 265 | blk.21.ffn_norm.weight | Block 21 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 266 | blk.21.ffn_up_exps.weight | Block 21 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.21: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_22">Block 22 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 267 | blk.22.attn_k.weight | Block 22 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 268 | blk.22.attn_k_norm.weight | Block 22 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 269 | blk.22.attn_norm.weight | Block 22 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 270 | blk.22.attn_output.weight | Block 22 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 271 | blk.22.attn_q.weight | Block 22 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 272 | blk.22.attn_q_norm.weight | Block 22 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 273 | blk.22.attn_v.weight | Block 22 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 274 | blk.22.ffn_down_exps.weight | Block 22 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 275 | blk.22.ffn_gate_exps.weight | Block 22 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 276 | blk.22.ffn_gate_inp.weight | Block 22 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 277 | blk.22.ffn_norm.weight | Block 22 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 278 | blk.22.ffn_up_exps.weight | Block 22 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.22: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_23">Block 23 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 279 | blk.23.attn_k.weight | Block 23 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ3_S |
| 280 | blk.23.attn_k_norm.weight | Block 23 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 281 | blk.23.attn_norm.weight | Block 23 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 282 | blk.23.attn_output.weight | Block 23 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 283 | blk.23.attn_q.weight | Block 23 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ3_S |
| 284 | blk.23.attn_q_norm.weight | Block 23 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 285 | blk.23.attn_v.weight | Block 23 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_XS |
| 286 | blk.23.ffn_down_exps.weight | Block 23 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 287 | blk.23.ffn_gate_exps.weight | Block 23 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 288 | blk.23.ffn_gate_inp.weight | Block 23 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 289 | blk.23.ffn_norm.weight | Block 23 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 290 | blk.23.ffn_up_exps.weight | Block 23 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.23: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_24">Block 24 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 291 | blk.24.attn_k.weight | Block 24 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 292 | blk.24.attn_k_norm.weight | Block 24 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 293 | blk.24.attn_norm.weight | Block 24 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 294 | blk.24.attn_output.weight | Block 24 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 295 | blk.24.attn_q.weight | Block 24 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 296 | blk.24.attn_q_norm.weight | Block 24 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 297 | blk.24.attn_v.weight | Block 24 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 298 | blk.24.ffn_down_exps.weight | Block 24 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 299 | blk.24.ffn_gate_exps.weight | Block 24 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 300 | blk.24.ffn_gate_inp.weight | Block 24 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 301 | blk.24.ffn_norm.weight | Block 24 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 302 | blk.24.ffn_up_exps.weight | Block 24 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
- Total elements in blk.24: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_25">Block 25 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 303 | blk.25.attn_k.weight | Block 25 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 304 | blk.25.attn_k_norm.weight | Block 25 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 305 | blk.25.attn_norm.weight | Block 25 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 306 | blk.25.attn_output.weight | Block 25 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 307 | blk.25.attn_q.weight | Block 25 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 308 | blk.25.attn_q_norm.weight | Block 25 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 309 | blk.25.attn_v.weight | Block 25 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 310 | blk.25.ffn_down_exps.weight | Block 25 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 311 | blk.25.ffn_gate_exps.weight | Block 25 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 312 | blk.25.ffn_gate_inp.weight | Block 25 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 313 | blk.25.ffn_norm.weight | Block 25 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 314 | blk.25.ffn_up_exps.weight | Block 25 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.25: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_26">Block 26 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 315 | blk.26.attn_k.weight | Block 26 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 316 | blk.26.attn_k_norm.weight | Block 26 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 317 | blk.26.attn_norm.weight | Block 26 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 318 | blk.26.attn_output.weight | Block 26 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 319 | blk.26.attn_q.weight | Block 26 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 320 | blk.26.attn_q_norm.weight | Block 26 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 321 | blk.26.attn_v.weight | Block 26 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 322 | blk.26.ffn_down_exps.weight | Block 26 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 323 | blk.26.ffn_gate_exps.weight | Block 26 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ3_S |
| 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 | IQ3_S |
- Total elements in blk.26: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_27">Block 27 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 327 | blk.27.attn_k.weight | Block 27 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 328 | blk.27.attn_k_norm.weight | Block 27 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 329 | blk.27.attn_norm.weight | Block 27 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 330 | blk.27.attn_output.weight | Block 27 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 331 | blk.27.attn_q.weight | Block 27 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 332 | blk.27.attn_q_norm.weight | Block 27 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 333 | blk.27.attn_v.weight | Block 27 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 334 | blk.27.ffn_down_exps.weight | Block 27 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 335 | blk.27.ffn_gate_exps.weight | Block 27 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 336 | blk.27.ffn_gate_inp.weight | Block 27 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 337 | blk.27.ffn_norm.weight | Block 27 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 338 | blk.27.ffn_up_exps.weight | Block 27 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.27: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_28">Block 28 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 339 | blk.28.attn_k.weight | Block 28 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 340 | blk.28.attn_k_norm.weight | Block 28 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 341 | blk.28.attn_norm.weight | Block 28 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 342 | blk.28.attn_output.weight | Block 28 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 343 | blk.28.attn_q.weight | Block 28 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 344 | blk.28.attn_q_norm.weight | Block 28 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 345 | blk.28.attn_v.weight | Block 28 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 346 | blk.28.ffn_down_exps.weight | Block 28 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 347 | blk.28.ffn_gate_exps.weight | Block 28 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 348 | blk.28.ffn_gate_inp.weight | Block 28 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 349 | blk.28.ffn_norm.weight | Block 28 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 350 | blk.28.ffn_up_exps.weight | Block 28 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.28: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_29">Block 29 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 351 | blk.29.attn_k.weight | Block 29 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 352 | blk.29.attn_k_norm.weight | Block 29 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 353 | blk.29.attn_norm.weight | Block 29 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 354 | blk.29.attn_output.weight | Block 29 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 355 | blk.29.attn_q.weight | Block 29 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 356 | blk.29.attn_q_norm.weight | Block 29 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 357 | blk.29.attn_v.weight | Block 29 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 358 | blk.29.ffn_down_exps.weight | Block 29 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 359 | blk.29.ffn_gate_exps.weight | Block 29 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 360 | blk.29.ffn_gate_inp.weight | Block 29 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 361 | blk.29.ffn_norm.weight | Block 29 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 362 | blk.29.ffn_up_exps.weight | Block 29 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.29: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_30">Block 30 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 363 | blk.30.attn_k.weight | Block 30 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 364 | blk.30.attn_k_norm.weight | Block 30 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 365 | blk.30.attn_norm.weight | Block 30 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 366 | blk.30.attn_output.weight | Block 30 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 367 | blk.30.attn_q.weight | Block 30 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 368 | blk.30.attn_q_norm.weight | Block 30 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 369 | blk.30.attn_v.weight | Block 30 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 370 | blk.30.ffn_down_exps.weight | Block 30 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 371 | blk.30.ffn_gate_exps.weight | Block 30 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 372 | blk.30.ffn_gate_inp.weight | Block 30 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 373 | blk.30.ffn_norm.weight | Block 30 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 374 | blk.30.ffn_up_exps.weight | Block 30 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.30: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_31">Block 31 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 375 | blk.31.attn_k.weight | Block 31 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 376 | blk.31.attn_k_norm.weight | Block 31 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 377 | blk.31.attn_norm.weight | Block 31 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 378 | blk.31.attn_output.weight | Block 31 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 379 | blk.31.attn_q.weight | Block 31 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 380 | blk.31.attn_q_norm.weight | Block 31 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 381 | blk.31.attn_v.weight | Block 31 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 382 | blk.31.ffn_down_exps.weight | Block 31 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 383 | blk.31.ffn_gate_exps.weight | Block 31 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 384 | blk.31.ffn_gate_inp.weight | Block 31 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 385 | blk.31.ffn_norm.weight | Block 31 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 386 | blk.31.ffn_up_exps.weight | Block 31 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.31: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_32">Block 32 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 387 | blk.32.attn_k.weight | Block 32 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 388 | blk.32.attn_k_norm.weight | Block 32 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 389 | blk.32.attn_norm.weight | Block 32 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 390 | blk.32.attn_output.weight | Block 32 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 391 | blk.32.attn_q.weight | Block 32 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 392 | blk.32.attn_q_norm.weight | Block 32 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 393 | blk.32.attn_v.weight | Block 32 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 394 | blk.32.ffn_down_exps.weight | Block 32 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 395 | blk.32.ffn_gate_exps.weight | Block 32 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 396 | blk.32.ffn_gate_inp.weight | Block 32 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 397 | blk.32.ffn_norm.weight | Block 32 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 398 | blk.32.ffn_up_exps.weight | Block 32 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.32: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_33">Block 33 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 399 | blk.33.attn_k.weight | Block 33 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 400 | blk.33.attn_k_norm.weight | Block 33 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 401 | blk.33.attn_norm.weight | Block 33 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 402 | blk.33.attn_output.weight | Block 33 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 403 | blk.33.attn_q.weight | Block 33 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 404 | blk.33.attn_q_norm.weight | Block 33 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 405 | blk.33.attn_v.weight | Block 33 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 406 | blk.33.ffn_down_exps.weight | Block 33 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 407 | blk.33.ffn_gate_exps.weight | Block 33 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 408 | blk.33.ffn_gate_inp.weight | Block 33 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 409 | blk.33.ffn_norm.weight | Block 33 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 410 | blk.33.ffn_up_exps.weight | Block 33 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.33: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_34">Block 34 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 411 | blk.34.attn_k.weight | Block 34 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 412 | blk.34.attn_k_norm.weight | Block 34 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 413 | blk.34.attn_norm.weight | Block 34 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 414 | blk.34.attn_output.weight | Block 34 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 415 | blk.34.attn_q.weight | Block 34 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 416 | blk.34.attn_q_norm.weight | Block 34 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 417 | blk.34.attn_v.weight | Block 34 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 418 | blk.34.ffn_down_exps.weight | Block 34 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 419 | blk.34.ffn_gate_exps.weight | Block 34 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 420 | blk.34.ffn_gate_inp.weight | Block 34 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 421 | blk.34.ffn_norm.weight | Block 34 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 422 | blk.34.ffn_up_exps.weight | Block 34 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.34: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_35">Block 35 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 423 | blk.35.attn_k.weight | Block 35 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 424 | blk.35.attn_k_norm.weight | Block 35 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 425 | blk.35.attn_norm.weight | Block 35 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 426 | blk.35.attn_output.weight | Block 35 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 427 | blk.35.attn_q.weight | Block 35 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 428 | blk.35.attn_q_norm.weight | Block 35 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 429 | blk.35.attn_v.weight | Block 35 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 430 | blk.35.ffn_down_exps.weight | Block 35 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 431 | blk.35.ffn_gate_exps.weight | Block 35 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 432 | blk.35.ffn_gate_inp.weight | Block 35 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 433 | blk.35.ffn_norm.weight | Block 35 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 434 | blk.35.ffn_up_exps.weight | Block 35 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.35: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_36">Block 36 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 435 | blk.36.attn_k.weight | Block 36 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 436 | blk.36.attn_k_norm.weight | Block 36 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 437 | blk.36.attn_norm.weight | Block 36 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 438 | blk.36.attn_output.weight | Block 36 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 439 | blk.36.attn_q.weight | Block 36 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 440 | blk.36.attn_q_norm.weight | Block 36 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 441 | blk.36.attn_v.weight | Block 36 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 442 | blk.36.ffn_down_exps.weight | Block 36 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 443 | blk.36.ffn_gate_exps.weight | Block 36 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 444 | blk.36.ffn_gate_inp.weight | Block 36 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 445 | blk.36.ffn_norm.weight | Block 36 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 446 | blk.36.ffn_up_exps.weight | Block 36 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.36: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_37">Block 37 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 447 | blk.37.attn_k.weight | Block 37 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 448 | blk.37.attn_k_norm.weight | Block 37 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 449 | blk.37.attn_norm.weight | Block 37 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 450 | blk.37.attn_output.weight | Block 37 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 451 | blk.37.attn_q.weight | Block 37 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 452 | blk.37.attn_q_norm.weight | Block 37 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 453 | blk.37.attn_v.weight | Block 37 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 454 | blk.37.ffn_down_exps.weight | Block 37 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 455 | blk.37.ffn_gate_exps.weight | Block 37 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 456 | blk.37.ffn_gate_inp.weight | Block 37 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 457 | blk.37.ffn_norm.weight | Block 37 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 458 | blk.37.ffn_up_exps.weight | Block 37 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.37: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_38">Block 38 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 459 | blk.38.attn_k.weight | Block 38 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 460 | blk.38.attn_k_norm.weight | Block 38 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 461 | blk.38.attn_norm.weight | Block 38 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 462 | blk.38.attn_output.weight | Block 38 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 463 | blk.38.attn_q.weight | Block 38 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 464 | blk.38.attn_q_norm.weight | Block 38 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 465 | blk.38.attn_v.weight | Block 38 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 466 | blk.38.ffn_down_exps.weight | Block 38 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 467 | blk.38.ffn_gate_exps.weight | Block 38 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 468 | blk.38.ffn_gate_inp.weight | Block 38 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 469 | blk.38.ffn_norm.weight | Block 38 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 470 | blk.38.ffn_up_exps.weight | Block 38 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.38: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_39">Block 39 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 471 | blk.39.attn_k.weight | Block 39 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 472 | blk.39.attn_k_norm.weight | Block 39 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 473 | blk.39.attn_norm.weight | Block 39 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 474 | blk.39.attn_output.weight | Block 39 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 475 | blk.39.attn_q.weight | Block 39 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 476 | blk.39.attn_q_norm.weight | Block 39 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 477 | blk.39.attn_v.weight | Block 39 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 478 | blk.39.ffn_down_exps.weight | Block 39 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 479 | blk.39.ffn_gate_exps.weight | Block 39 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 480 | blk.39.ffn_gate_inp.weight | Block 39 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 481 | blk.39.ffn_norm.weight | Block 39 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 482 | blk.39.ffn_up_exps.weight | Block 39 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.39: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_40">Block 40 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 483 | blk.40.attn_k.weight | Block 40 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 484 | blk.40.attn_k_norm.weight | Block 40 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 485 | blk.40.attn_norm.weight | Block 40 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 486 | blk.40.attn_output.weight | Block 40 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 487 | blk.40.attn_q.weight | Block 40 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 488 | blk.40.attn_q_norm.weight | Block 40 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 489 | blk.40.attn_v.weight | Block 40 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 490 | blk.40.ffn_down_exps.weight | Block 40 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 491 | blk.40.ffn_gate_exps.weight | Block 40 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 492 | blk.40.ffn_gate_inp.weight | Block 40 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 493 | blk.40.ffn_norm.weight | Block 40 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 494 | blk.40.ffn_up_exps.weight | Block 40 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.40: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_41">Block 41 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 495 | blk.41.attn_k.weight | Block 41 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 496 | blk.41.attn_k_norm.weight | Block 41 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 497 | blk.41.attn_norm.weight | Block 41 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 498 | blk.41.attn_output.weight | Block 41 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 499 | blk.41.attn_q.weight | Block 41 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 500 | blk.41.attn_q_norm.weight | Block 41 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 501 | blk.41.attn_v.weight | Block 41 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 502 | blk.41.ffn_down_exps.weight | Block 41 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 503 | blk.41.ffn_gate_exps.weight | Block 41 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 504 | blk.41.ffn_gate_inp.weight | Block 41 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 505 | blk.41.ffn_norm.weight | Block 41 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 506 | blk.41.ffn_up_exps.weight | Block 41 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.41: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_42">Block 42 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 507 | blk.42.attn_k.weight | Block 42 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 508 | blk.42.attn_k_norm.weight | Block 42 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 509 | blk.42.attn_norm.weight | Block 42 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 510 | blk.42.attn_output.weight | Block 42 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 511 | blk.42.attn_q.weight | Block 42 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 512 | blk.42.attn_q_norm.weight | Block 42 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 513 | blk.42.attn_v.weight | Block 42 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 514 | blk.42.ffn_down_exps.weight | Block 42 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 515 | blk.42.ffn_gate_exps.weight | Block 42 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 516 | blk.42.ffn_gate_inp.weight | Block 42 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 517 | blk.42.ffn_norm.weight | Block 42 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 518 | blk.42.ffn_up_exps.weight | Block 42 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.42: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_43">Block 43 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 519 | blk.43.attn_k.weight | Block 43 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 520 | blk.43.attn_k_norm.weight | Block 43 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 521 | blk.43.attn_norm.weight | Block 43 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 522 | blk.43.attn_output.weight | Block 43 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 523 | blk.43.attn_q.weight | Block 43 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 524 | blk.43.attn_q_norm.weight | Block 43 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 525 | blk.43.attn_v.weight | Block 43 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 526 | blk.43.ffn_down_exps.weight | Block 43 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 527 | blk.43.ffn_gate_exps.weight | Block 43 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 528 | blk.43.ffn_gate_inp.weight | Block 43 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 529 | blk.43.ffn_norm.weight | Block 43 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 530 | blk.43.ffn_up_exps.weight | Block 43 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.43: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_44">Block 44 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 531 | blk.44.attn_k.weight | Block 44 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 532 | blk.44.attn_k_norm.weight | Block 44 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 533 | blk.44.attn_norm.weight | Block 44 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 534 | blk.44.attn_output.weight | Block 44 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 535 | blk.44.attn_q.weight | Block 44 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 536 | blk.44.attn_q_norm.weight | Block 44 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 537 | blk.44.attn_v.weight | Block 44 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 538 | blk.44.ffn_down_exps.weight | Block 44 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 539 | blk.44.ffn_gate_exps.weight | Block 44 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 540 | blk.44.ffn_gate_inp.weight | Block 44 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 541 | blk.44.ffn_norm.weight | Block 44 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 542 | blk.44.ffn_up_exps.weight | Block 44 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.44: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_45">Block 45 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 543 | blk.45.attn_k.weight | Block 45 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 544 | blk.45.attn_k_norm.weight | Block 45 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 545 | blk.45.attn_norm.weight | Block 45 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 546 | blk.45.attn_output.weight | Block 45 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 547 | blk.45.attn_q.weight | Block 45 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 548 | blk.45.attn_q_norm.weight | Block 45 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 549 | blk.45.attn_v.weight | Block 45 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 550 | blk.45.ffn_down_exps.weight | Block 45 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 551 | blk.45.ffn_gate_exps.weight | Block 45 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 552 | blk.45.ffn_gate_inp.weight | Block 45 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 553 | blk.45.ffn_norm.weight | Block 45 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 554 | blk.45.ffn_up_exps.weight | Block 45 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.45: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_46">Block 46 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 555 | blk.46.attn_k.weight | Block 46 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 556 | blk.46.attn_k_norm.weight | Block 46 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 557 | blk.46.attn_norm.weight | Block 46 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 558 | blk.46.attn_output.weight | Block 46 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 559 | blk.46.attn_q.weight | Block 46 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 560 | blk.46.attn_q_norm.weight | Block 46 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 561 | blk.46.attn_v.weight | Block 46 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 562 | blk.46.ffn_down_exps.weight | Block 46 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 563 | blk.46.ffn_gate_exps.weight | Block 46 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 564 | blk.46.ffn_gate_inp.weight | Block 46 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 565 | blk.46.ffn_norm.weight | Block 46 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 566 | blk.46.ffn_up_exps.weight | Block 46 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.46: (~623M) 623120640
- Percentage of total elements: 2.04%
### <a name="blk_47">Block 47 Tensor Group : ~623M Elements</a>
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|-----:|:----------------------------|:-------------------------------------------------------------------------------------------|:------------------|:----------------------|:-------|
| 567 | blk.47.attn_k.weight | Block 47 Attention Key (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 568 | blk.47.attn_k_norm.weight | Block 47 Attn_K_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 569 | blk.47.attn_norm.weight | Block 47 Attention Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 570 | blk.47.attn_output.weight | Block 47 Attention Output (W) | ( ~8M) 8388608 | 4096 x 2048 x 1 x 1 | IQ4_NL |
| 571 | blk.47.attn_q.weight | Block 47 Attention Query (W) | ( ~8M) 8388608 | 2048 x 4096 x 1 x 1 | IQ4_NL |
| 572 | blk.47.attn_q_norm.weight | Block 47 Attn_Q_Norm (W) | ( 128) 128 | 128 x 1 x 1 x 1 | F32 |
| 573 | blk.47.attn_v.weight | Block 47 Attention Value (W) | ( ~1M) 1048576 | 2048 x 512 x 1 x 1 | IQ4_NL |
| 574 | blk.47.ffn_down_exps.weight | Block 47 Ffn_Down_Exps (W) | (~201M) 201326592 | 768 x 2048 x 128 x 1 | Q5_K |
| 575 | blk.47.ffn_gate_exps.weight | Block 47 Ffn_Gate_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
| 576 | blk.47.ffn_gate_inp.weight | Block 47 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W) | (~262K) 262144 | 2048 x 128 x 1 x 1 | F32 |
| 577 | blk.47.ffn_norm.weight | Block 47 Feed-Forward Network Normalization (W) | ( ~2K) 2048 | 2048 x 1 x 1 x 1 | F32 |
| 578 | blk.47.ffn_up_exps.weight | Block 47 Ffn_Up_Exps (W) | (~201M) 201326592 | 2048 x 768 x 128 x 1 | IQ4_NL |
- Total elements in blk.47: (~623M) 623120640
- Percentage of total elements: 2.04%
|