TinyLLama-4.6M-v0.0-F16.gguf - GGUF Internal File Dump
- Endian: LITTLE endian
Key Value Metadata Store
There are 40 key-value pairs in this file
| POS | TYPE | Count | Key | Value |
|---|---|---|---|---|
| 1 | UINT32 | 1 | GGUF.version | 3 |
| 2 | UINT64 | 1 | GGUF.tensor_count | 75 |
| 3 | UINT64 | 1 | GGUF.kv_count | 37 |
| 4 | STRING | 1 | general.architecture | llama |
| 5 | STRING | 1 | general.type | model |
| 6 | STRING | 1 | general.name | TinyLLama |
| 7 | STRING | 1 | general.author | Maykeye |
| 8 | STRING | 1 | general.version | v0.0 |
| 9 | STRING | 1 | general.description | This gguf is ported from a fir...M but using Llama architecture |
| 10 | STRING | 1 | general.quantized_by | Mofosyne |
| 11 | STRING | 1 | general.size_label | 4.6M |
| 12 | STRING | 1 | general.license | apache-2.0 |
| 13 | STRING | 1 | general.license.name | Apache License Version 2.0, January 2004 |
| 14 | STRING | 1 | general.license.link | https://huggingface.co/dataset...ob/main/markdown/apache-2.0.md |
| 15 | STRING | 1 | general.url | https://huggingface.co/mofosyne/TinyLLama-v0-llamafile |
| 16 | STRING | 1 | general.repo_url | https://huggingface.co/mofosyne/TinyLLama-v0-llamafile |
| 17 | STRING | 1 | general.source.url | https://huggingface.co/Maykeye/TinyLLama-v0 |
| 18 | STRING | 1 | general.source.repo_url | https://huggingface.co/Maykeye/TinyLLama-v0 |
| 19 | [STRING] | 5 | general.tags | [ text generation, transformer, llama, tiny, tiny model ] |
| 20 | [STRING] | 1 | general.languages | [ en ] |
| 21 | [STRING] | 2 | general.datasets | [ https://hugging...-GPT4-train.txt, https://hugging...-GPT4-valid.txt ] |
| 22 | UINT32 | 1 | llama.block_count | 8 |
| 23 | UINT32 | 1 | llama.context_length | 2048 |
| 24 | UINT32 | 1 | llama.embedding_length | 64 |
| 25 | UINT32 | 1 | llama.feed_forward_length | 256 |
| 26 | UINT32 | 1 | llama.attention.head_count | 16 |
| 27 | FLOAT32 | 1 | llama.attention.layer_norm_rms_epsilon | 1e-06 |
| 28 | UINT32 | 1 | general.file_type | 1 |
| 29 | UINT32 | 1 | llama.vocab_size | 32000 |
| 30 | UINT32 | 1 | llama.rope.dimension_count | 4 |
| 31 | STRING | 1 | tokenizer.ggml.model | llama |
| 32 | STRING | 1 | tokenizer.ggml.pre | default |
| 33 | [STRING] | 32000 | tokenizer.ggml.tokens | [ <unk>, <s>, </s>, <0x00>, <0x01>, ... ] |
| 34 | [FLOAT32] | 32000 | tokenizer.ggml.scores | [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] |
| 35 | [INT32] | 32000 | tokenizer.ggml.token_type | [ 2, 3, 3, 6, 6, 6, 6, ... ] |
| 36 | UINT32 | 1 | tokenizer.ggml.bos_token_id | 1 |
| 37 | UINT32 | 1 | tokenizer.ggml.eos_token_id | 2 |
| 38 | UINT32 | 1 | tokenizer.ggml.unknown_token_id | 0 |
| 39 | UINT32 | 1 | tokenizer.ggml.padding_token_id | 0 |
| 40 | UINT32 | 1 | general.quantization_version | 2 |
Tensors Overview ~5M Elements
Total number of elements in all tensors: 4621376 Elements
- Base Tensor Group - ~4M Elements
- Block 0 Tensor Group - ~66K Elements
- Block 1 Tensor Group - ~66K Elements
- Block 2 Tensor Group - ~66K Elements
- Block 3 Tensor Group - ~66K Elements
- Block 4 Tensor Group - ~66K Elements
- Block 5 Tensor Group - ~66K Elements
- Block 6 Tensor Group - ~66K Elements
- Block 7 Tensor Group - ~66K 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 | 0xba8e0 | 0x3e8000 |
| 1 | token_embd.weight | 0x4a28e0 | 0x3e8000 |
| 2 | blk.0.attn_norm.weight | 0x88a8e0 | 0x100 |
| 3 | blk.0.ffn_down.weight | 0x88a9e0 | 0x8000 |
| 4 | blk.0.ffn_gate.weight | 0x8929e0 | 0x8000 |
| 5 | blk.0.ffn_up.weight | 0x89a9e0 | 0x8000 |
| 6 | blk.0.ffn_norm.weight | 0x8a29e0 | 0x100 |
| 7 | blk.0.attn_k.weight | 0x8a2ae0 | 0x2000 |
| 8 | blk.0.attn_output.weight | 0x8a4ae0 | 0x2000 |
| 9 | blk.0.attn_q.weight | 0x8a6ae0 | 0x2000 |
| 10 | blk.0.attn_v.weight | 0x8a8ae0 | 0x2000 |
| 11 | blk.1.attn_norm.weight | 0x8aaae0 | 0x100 |
| 12 | blk.1.ffn_down.weight | 0x8aabe0 | 0x8000 |
| 13 | blk.1.ffn_gate.weight | 0x8b2be0 | 0x8000 |
| 14 | blk.1.ffn_up.weight | 0x8babe0 | 0x8000 |
| 15 | blk.1.ffn_norm.weight | 0x8c2be0 | 0x100 |
| 16 | blk.1.attn_k.weight | 0x8c2ce0 | 0x2000 |
| 17 | blk.1.attn_output.weight | 0x8c4ce0 | 0x2000 |
| 18 | blk.1.attn_q.weight | 0x8c6ce0 | 0x2000 |
| 19 | blk.1.attn_v.weight | 0x8c8ce0 | 0x2000 |
| 20 | blk.2.attn_norm.weight | 0x8cace0 | 0x100 |
| 21 | blk.2.ffn_down.weight | 0x8cade0 | 0x8000 |
| 22 | blk.2.ffn_gate.weight | 0x8d2de0 | 0x8000 |
| 23 | blk.2.ffn_up.weight | 0x8dade0 | 0x8000 |
| 24 | blk.2.ffn_norm.weight | 0x8e2de0 | 0x100 |
| 25 | blk.2.attn_k.weight | 0x8e2ee0 | 0x2000 |
| 26 | blk.2.attn_output.weight | 0x8e4ee0 | 0x2000 |
| 27 | blk.2.attn_q.weight | 0x8e6ee0 | 0x2000 |
| 28 | blk.2.attn_v.weight | 0x8e8ee0 | 0x2000 |
| 29 | blk.3.attn_norm.weight | 0x8eaee0 | 0x100 |
| 30 | blk.3.ffn_down.weight | 0x8eafe0 | 0x8000 |
| 31 | blk.3.ffn_gate.weight | 0x8f2fe0 | 0x8000 |
| 32 | blk.3.ffn_up.weight | 0x8fafe0 | 0x8000 |
| 33 | blk.3.ffn_norm.weight | 0x902fe0 | 0x100 |
| 34 | blk.3.attn_k.weight | 0x9030e0 | 0x2000 |
| 35 | blk.3.attn_output.weight | 0x9050e0 | 0x2000 |
| 36 | blk.3.attn_q.weight | 0x9070e0 | 0x2000 |
| 37 | blk.3.attn_v.weight | 0x9090e0 | 0x2000 |
| 38 | blk.4.attn_norm.weight | 0x90b0e0 | 0x100 |
| 39 | blk.4.ffn_down.weight | 0x90b1e0 | 0x8000 |
| 40 | blk.4.ffn_gate.weight | 0x9131e0 | 0x8000 |
| 41 | blk.4.ffn_up.weight | 0x91b1e0 | 0x8000 |
| 42 | blk.4.ffn_norm.weight | 0x9231e0 | 0x100 |
| 43 | blk.4.attn_k.weight | 0x9232e0 | 0x2000 |
| 44 | blk.4.attn_output.weight | 0x9252e0 | 0x2000 |
| 45 | blk.4.attn_q.weight | 0x9272e0 | 0x2000 |
| 46 | blk.4.attn_v.weight | 0x9292e0 | 0x2000 |
| 47 | blk.5.attn_norm.weight | 0x92b2e0 | 0x100 |
| 48 | blk.5.ffn_down.weight | 0x92b3e0 | 0x8000 |
| 49 | blk.5.ffn_gate.weight | 0x9333e0 | 0x8000 |
| 50 | blk.5.ffn_up.weight | 0x93b3e0 | 0x8000 |
| 51 | blk.5.ffn_norm.weight | 0x9433e0 | 0x100 |
| 52 | blk.5.attn_k.weight | 0x9434e0 | 0x2000 |
| 53 | blk.5.attn_output.weight | 0x9454e0 | 0x2000 |
| 54 | blk.5.attn_q.weight | 0x9474e0 | 0x2000 |
| 55 | blk.5.attn_v.weight | 0x9494e0 | 0x2000 |
| 56 | blk.6.attn_norm.weight | 0x94b4e0 | 0x100 |
| 57 | blk.6.ffn_down.weight | 0x94b5e0 | 0x8000 |
| 58 | blk.6.ffn_gate.weight | 0x9535e0 | 0x8000 |
| 59 | blk.6.ffn_up.weight | 0x95b5e0 | 0x8000 |
| 60 | blk.6.ffn_norm.weight | 0x9635e0 | 0x100 |
| 61 | blk.6.attn_k.weight | 0x9636e0 | 0x2000 |
| 62 | blk.6.attn_output.weight | 0x9656e0 | 0x2000 |
| 63 | blk.6.attn_q.weight | 0x9676e0 | 0x2000 |
| 64 | blk.6.attn_v.weight | 0x9696e0 | 0x2000 |
| 65 | blk.7.attn_norm.weight | 0x96b6e0 | 0x100 |
| 66 | blk.7.ffn_down.weight | 0x96b7e0 | 0x8000 |
| 67 | blk.7.ffn_gate.weight | 0x9737e0 | 0x8000 |
| 68 | blk.7.ffn_up.weight | 0x97b7e0 | 0x8000 |
| 69 | blk.7.ffn_norm.weight | 0x9837e0 | 0x100 |
| 70 | blk.7.attn_k.weight | 0x9838e0 | 0x2000 |
| 71 | blk.7.attn_output.weight | 0x9858e0 | 0x2000 |
| 72 | blk.7.attn_q.weight | 0x9878e0 | 0x2000 |
| 73 | blk.7.attn_v.weight | 0x9898e0 | 0x2000 |
| 74 | output_norm.weight | 0x98b8e0 | 0x100 |
Base Tensor Group : ~4M Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 0 | output.weight | Output (W) | (~2M) 2048000 | 64 x 32000 x 1 x 1 | F16 |
| 1 | token_embd.weight | Token Embedding (W) | (~2M) 2048000 | 64 x 32000 x 1 x 1 | F16 |
| 74 | output_norm.weight | Output Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
- Total elements in base: ( ~4M) 4096064
- Percentage of total elements: 88.63%
Block 0 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 2 | blk.0.attn_norm.weight | Block 0 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 3 | blk.0.ffn_down.weight | Block 0 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 4 | blk.0.ffn_gate.weight | Block 0 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 5 | blk.0.ffn_up.weight | Block 0 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 6 | blk.0.ffn_norm.weight | Block 0 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 7 | blk.0.attn_k.weight | Block 0 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 8 | blk.0.attn_output.weight | Block 0 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 9 | blk.0.attn_q.weight | Block 0 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 10 | blk.0.attn_v.weight | Block 0 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.0: (~66K) 65664
- Percentage of total elements: 1.42%
Block 1 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 11 | blk.1.attn_norm.weight | Block 1 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 12 | blk.1.ffn_down.weight | Block 1 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 13 | blk.1.ffn_gate.weight | Block 1 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 14 | blk.1.ffn_up.weight | Block 1 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 15 | blk.1.ffn_norm.weight | Block 1 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 16 | blk.1.attn_k.weight | Block 1 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 17 | blk.1.attn_output.weight | Block 1 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 18 | blk.1.attn_q.weight | Block 1 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 19 | blk.1.attn_v.weight | Block 1 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.1: (~66K) 65664
- Percentage of total elements: 1.42%
Block 2 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 20 | blk.2.attn_norm.weight | Block 2 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 21 | blk.2.ffn_down.weight | Block 2 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 22 | blk.2.ffn_gate.weight | Block 2 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 23 | blk.2.ffn_up.weight | Block 2 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 24 | blk.2.ffn_norm.weight | Block 2 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 25 | blk.2.attn_k.weight | Block 2 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 26 | blk.2.attn_output.weight | Block 2 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 27 | blk.2.attn_q.weight | Block 2 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 28 | blk.2.attn_v.weight | Block 2 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.2: (~66K) 65664
- Percentage of total elements: 1.42%
Block 3 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 29 | blk.3.attn_norm.weight | Block 3 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 30 | blk.3.ffn_down.weight | Block 3 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 31 | blk.3.ffn_gate.weight | Block 3 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 32 | blk.3.ffn_up.weight | Block 3 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 33 | blk.3.ffn_norm.weight | Block 3 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 34 | blk.3.attn_k.weight | Block 3 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 35 | blk.3.attn_output.weight | Block 3 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 36 | blk.3.attn_q.weight | Block 3 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 37 | blk.3.attn_v.weight | Block 3 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.3: (~66K) 65664
- Percentage of total elements: 1.42%
Block 4 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 38 | blk.4.attn_norm.weight | Block 4 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 39 | blk.4.ffn_down.weight | Block 4 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 40 | blk.4.ffn_gate.weight | Block 4 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 41 | blk.4.ffn_up.weight | Block 4 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 42 | blk.4.ffn_norm.weight | Block 4 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 43 | blk.4.attn_k.weight | Block 4 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 44 | blk.4.attn_output.weight | Block 4 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 45 | blk.4.attn_q.weight | Block 4 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 46 | blk.4.attn_v.weight | Block 4 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.4: (~66K) 65664
- Percentage of total elements: 1.42%
Block 5 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 47 | blk.5.attn_norm.weight | Block 5 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 48 | blk.5.ffn_down.weight | Block 5 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 49 | blk.5.ffn_gate.weight | Block 5 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 50 | blk.5.ffn_up.weight | Block 5 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 51 | blk.5.ffn_norm.weight | Block 5 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 52 | blk.5.attn_k.weight | Block 5 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 53 | blk.5.attn_output.weight | Block 5 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 54 | blk.5.attn_q.weight | Block 5 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 55 | blk.5.attn_v.weight | Block 5 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.5: (~66K) 65664
- Percentage of total elements: 1.42%
Block 6 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 56 | blk.6.attn_norm.weight | Block 6 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 57 | blk.6.ffn_down.weight | Block 6 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 58 | blk.6.ffn_gate.weight | Block 6 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 59 | blk.6.ffn_up.weight | Block 6 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 60 | blk.6.ffn_norm.weight | Block 6 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 61 | blk.6.attn_k.weight | Block 6 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 62 | blk.6.attn_output.weight | Block 6 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 63 | blk.6.attn_q.weight | Block 6 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 64 | blk.6.attn_v.weight | Block 6 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.6: (~66K) 65664
- Percentage of total elements: 1.42%
Block 7 Tensor Group : ~66K Elements
| T_ID | Tensor Layer Name | Human Friendly Tensor Layer Name | Elements | Shape | Type |
|---|---|---|---|---|---|
| 65 | blk.7.attn_norm.weight | Block 7 Attention Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 66 | blk.7.ffn_down.weight | Block 7 Feed-Forward Network "Down" (W) | (~16K) 16384 | 256 x 64 x 1 x 1 | F16 |
| 67 | blk.7.ffn_gate.weight | Block 7 Feed-Forward Network "Gate" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 68 | blk.7.ffn_up.weight | Block 7 Feed-Forward Network "Up" (W) | (~16K) 16384 | 64 x 256 x 1 x 1 | F16 |
| 69 | blk.7.ffn_norm.weight | Block 7 Feed-Forward Network Normalization (W) | ( 64) 64 | 64 x 1 x 1 x 1 | F32 |
| 70 | blk.7.attn_k.weight | Block 7 Attention Key (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 71 | blk.7.attn_output.weight | Block 7 Attention Output (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 72 | blk.7.attn_q.weight | Block 7 Attention Query (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
| 73 | blk.7.attn_v.weight | Block 7 Attention Value (W) | ( ~4K) 4096 | 64 x 64 x 1 x 1 | F16 |
- Total elements in blk.7: (~66K) 65664
- Percentage of total elements: 1.42%