Maykeye_Tinyllama-4.6M-v0.0-F16.gguf - GGUF Internal File Dump
- Endian: LITTLE endian
Key Value Metadata Store
There are 36 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 | 33 |
| 4 | STRING | 1 | general.architecture | 'llama' |
| 5 | STRING | 1 | general.type | 'model' |
| 6 | STRING | 1 | general.name | 'Maykeye_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 first version of Maykeye attempt ' |
| 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.url | 'https://huggingface.co/mofosyne/TinyLLama-v0-llamafile' |
| 14 | STRING | 1 | general.source.url | 'https://huggingface.co/Maykeye/TinyLLama-v0' |
| 15 | [STRING] | 5 | general.tags | [ 'tiny ', '\n\x00\x00\x00\x00', 'tiny', '\x04\x00\x00\x00\x00', 'llama', ... ] |
| 16 | [STRING] | 1 | general.languages | [ 'en' ] |
| 17 | [STRING] | 2 | general.datasets | [ 'https', ']\x00\x00\x00\x00', ... ] |
| 18 | UINT32 | 1 | llama.block_count | 8 |
| 19 | UINT32 | 1 | llama.context_length | 2048 |
| 20 | UINT32 | 1 | llama.embedding_length | 64 |
| 21 | UINT32 | 1 | llama.feed_forward_length | 256 |
| 22 | UINT32 | 1 | llama.attention.head_count | 16 |
| 23 | FLOAT32 | 1 | llama.attention.layer_norm_rms_epsilon | 1e-06 |
| 24 | UINT32 | 1 | general.file_type | 1 |
| 25 | UINT32 | 1 | llama.vocab_size | 32000 |
| 26 | UINT32 | 1 | llama.rope.dimension_count | 4 |
| 27 | STRING | 1 | tokenizer.ggml.model | 'llama' |
| 28 | STRING | 1 | tokenizer.ggml.pre | 'default' |
| 29 | [STRING] | 32000 | tokenizer.ggml.tokens | [ 'А', '\x02\x00\x00\x00\x00', 'š', '\x02\x00\x00\x00\x00', 'α', ... ] |
| 30 | [FLOAT32] | 32000 | tokenizer.ggml.scores | [ -31740.0, -31739.0, -31738.0, -31737.0, -31736.0, -31735.0, -31734.0, ... ] |
| 31 | [INT32] | 32000 | tokenizer.ggml.token_type | [ 1, 1, 1, 1, 1, 1, 1, ... ] |
| 32 | UINT32 | 1 | tokenizer.ggml.bos_token_id | 1 |
| 33 | UINT32 | 1 | tokenizer.ggml.eos_token_id | 2 |
| 34 | UINT32 | 1 | tokenizer.ggml.unknown_token_id | 0 |
| 35 | UINT32 | 1 | tokenizer.ggml.padding_token_id | 0 |
| 36 | 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 | 0xba760 | 0x3e8000 |
| 1 | token_embd.weight | 0x4a2760 | 0x3e8000 |
| 2 | blk.0.attn_norm.weight | 0x88a760 | 0x100 |
| 3 | blk.0.ffn_down.weight | 0x88a860 | 0x8000 |
| 4 | blk.0.ffn_gate.weight | 0x892860 | 0x8000 |
| 5 | blk.0.ffn_up.weight | 0x89a860 | 0x8000 |
| 6 | blk.0.ffn_norm.weight | 0x8a2860 | 0x100 |
| 7 | blk.0.attn_k.weight | 0x8a2960 | 0x2000 |
| 8 | blk.0.attn_output.weight | 0x8a4960 | 0x2000 |
| 9 | blk.0.attn_q.weight | 0x8a6960 | 0x2000 |
| 10 | blk.0.attn_v.weight | 0x8a8960 | 0x2000 |
| 11 | blk.1.attn_norm.weight | 0x8aa960 | 0x100 |
| 12 | blk.1.ffn_down.weight | 0x8aaa60 | 0x8000 |
| 13 | blk.1.ffn_gate.weight | 0x8b2a60 | 0x8000 |
| 14 | blk.1.ffn_up.weight | 0x8baa60 | 0x8000 |
| 15 | blk.1.ffn_norm.weight | 0x8c2a60 | 0x100 |
| 16 | blk.1.attn_k.weight | 0x8c2b60 | 0x2000 |
| 17 | blk.1.attn_output.weight | 0x8c4b60 | 0x2000 |
| 18 | blk.1.attn_q.weight | 0x8c6b60 | 0x2000 |
| 19 | blk.1.attn_v.weight | 0x8c8b60 | 0x2000 |
| 20 | blk.2.attn_norm.weight | 0x8cab60 | 0x100 |
| 21 | blk.2.ffn_down.weight | 0x8cac60 | 0x8000 |
| 22 | blk.2.ffn_gate.weight | 0x8d2c60 | 0x8000 |
| 23 | blk.2.ffn_up.weight | 0x8dac60 | 0x8000 |
| 24 | blk.2.ffn_norm.weight | 0x8e2c60 | 0x100 |
| 25 | blk.2.attn_k.weight | 0x8e2d60 | 0x2000 |
| 26 | blk.2.attn_output.weight | 0x8e4d60 | 0x2000 |
| 27 | blk.2.attn_q.weight | 0x8e6d60 | 0x2000 |
| 28 | blk.2.attn_v.weight | 0x8e8d60 | 0x2000 |
| 29 | blk.3.attn_norm.weight | 0x8ead60 | 0x100 |
| 30 | blk.3.ffn_down.weight | 0x8eae60 | 0x8000 |
| 31 | blk.3.ffn_gate.weight | 0x8f2e60 | 0x8000 |
| 32 | blk.3.ffn_up.weight | 0x8fae60 | 0x8000 |
| 33 | blk.3.ffn_norm.weight | 0x902e60 | 0x100 |
| 34 | blk.3.attn_k.weight | 0x902f60 | 0x2000 |
| 35 | blk.3.attn_output.weight | 0x904f60 | 0x2000 |
| 36 | blk.3.attn_q.weight | 0x906f60 | 0x2000 |
| 37 | blk.3.attn_v.weight | 0x908f60 | 0x2000 |
| 38 | blk.4.attn_norm.weight | 0x90af60 | 0x100 |
| 39 | blk.4.ffn_down.weight | 0x90b060 | 0x8000 |
| 40 | blk.4.ffn_gate.weight | 0x913060 | 0x8000 |
| 41 | blk.4.ffn_up.weight | 0x91b060 | 0x8000 |
| 42 | blk.4.ffn_norm.weight | 0x923060 | 0x100 |
| 43 | blk.4.attn_k.weight | 0x923160 | 0x2000 |
| 44 | blk.4.attn_output.weight | 0x925160 | 0x2000 |
| 45 | blk.4.attn_q.weight | 0x927160 | 0x2000 |
| 46 | blk.4.attn_v.weight | 0x929160 | 0x2000 |
| 47 | blk.5.attn_norm.weight | 0x92b160 | 0x100 |
| 48 | blk.5.ffn_down.weight | 0x92b260 | 0x8000 |
| 49 | blk.5.ffn_gate.weight | 0x933260 | 0x8000 |
| 50 | blk.5.ffn_up.weight | 0x93b260 | 0x8000 |
| 51 | blk.5.ffn_norm.weight | 0x943260 | 0x100 |
| 52 | blk.5.attn_k.weight | 0x943360 | 0x2000 |
| 53 | blk.5.attn_output.weight | 0x945360 | 0x2000 |
| 54 | blk.5.attn_q.weight | 0x947360 | 0x2000 |
| 55 | blk.5.attn_v.weight | 0x949360 | 0x2000 |
| 56 | blk.6.attn_norm.weight | 0x94b360 | 0x100 |
| 57 | blk.6.ffn_down.weight | 0x94b460 | 0x8000 |
| 58 | blk.6.ffn_gate.weight | 0x953460 | 0x8000 |
| 59 | blk.6.ffn_up.weight | 0x95b460 | 0x8000 |
| 60 | blk.6.ffn_norm.weight | 0x963460 | 0x100 |
| 61 | blk.6.attn_k.weight | 0x963560 | 0x2000 |
| 62 | blk.6.attn_output.weight | 0x965560 | 0x2000 |
| 63 | blk.6.attn_q.weight | 0x967560 | 0x2000 |
| 64 | blk.6.attn_v.weight | 0x969560 | 0x2000 |
| 65 | blk.7.attn_norm.weight | 0x96b560 | 0x100 |
| 66 | blk.7.ffn_down.weight | 0x96b660 | 0x8000 |
| 67 | blk.7.ffn_gate.weight | 0x973660 | 0x8000 |
| 68 | blk.7.ffn_up.weight | 0x97b660 | 0x8000 |
| 69 | blk.7.ffn_norm.weight | 0x983660 | 0x100 |
| 70 | blk.7.attn_k.weight | 0x983760 | 0x2000 |
| 71 | blk.7.attn_output.weight | 0x985760 | 0x2000 |
| 72 | blk.7.attn_q.weight | 0x987760 | 0x2000 |
| 73 | blk.7.attn_v.weight | 0x989760 | 0x2000 |
| 74 | output_norm.weight | 0x98b760 | 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%