Instructions to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Ollama
How to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with Ollama:
```
ollama run hf.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
```

Lemonade

How to use eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Llama-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

DeepSeek-R1-Distill-Llama-8B-GGUF / scores /DeepSeek-R1-Distill-Llama-8B-Q3_K_L.md

eaddario

Add GGUF internal file structure

66279ab verified over 1 year ago

preview code

Raw

History Blame

83.1 kB

	# DeepSeek-R1-Distill-Llama-8B-Q3_K_L.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 \| 292 \|
	\| 3 \| UINT64 \| 1 \| GGUF.kv_count \| 33 \|
	\| 4 \| STRING \| 1 \| general.architecture \| `llama` \|
	\| 5 \| STRING \| 1 \| general.type \| `model` \|
	\| 6 \| STRING \| 1 \| general.name \| `DeepSeek R1 Distill Llama 8B` \|
	\| 7 \| STRING \| 1 \| general.basename \| `DeepSeek-R1-Distill-Llama` \|
	\| 8 \| STRING \| 1 \| general.size_label \| `8B` \|
	\| 9 \| STRING \| 1 \| general.license \| `mit` \|
	\| 10 \| UINT32 \| 1 \| llama.block_count \| 32 \|
	\| 11 \| UINT32 \| 1 \| llama.context_length \| 131072 \|
	\| 12 \| UINT32 \| 1 \| llama.embedding_length \| 4096 \|
	\| 13 \| UINT32 \| 1 \| llama.feed_forward_length \| 14336 \|
	\| 14 \| UINT32 \| 1 \| llama.attention.head_count \| 32 \|
	\| 15 \| UINT32 \| 1 \| llama.attention.head_count_kv \| 8 \|
	\| 16 \| FLOAT32 \| 1 \| llama.rope.freq_base \| 500000.0 \|
	\| 17 \| FLOAT32 \| 1 \| llama.attention.layer_norm_rms_epsilon \| 1e-05 \|
	\| 18 \| UINT32 \| 1 \| llama.vocab_size \| 128256 \|
	\| 19 \| UINT32 \| 1 \| llama.rope.dimension_count \| 128 \|
	\| 20 \| STRING \| 1 \| tokenizer.ggml.model \| `gpt2` \|
	\| 21 \| STRING \| 1 \| tokenizer.ggml.pre \| `llama-bpe` \|
	\| 22 \| [STRING] \| 128256 \| tokenizer.ggml.tokens \| [ `!`, `"`, `#`, `$`, `%`, ... ] \|
	\| 23 \| [INT32] \| 128256 \| tokenizer.ggml.token_type \| [ 1, 1, 1, 1, 1, 1, 1, ... ] \|
	\| 24 \| [STRING] \| 280147 \| tokenizer.ggml.merges \| [ `Ġ Ġ`, `Ġ ĠĠĠ`, `ĠĠ ĠĠ`, `ĠĠĠ Ġ`, `i n`, ... ] \|
	\| 25 \| UINT32 \| 1 \| tokenizer.ggml.bos_token_id \| 128000 \|
	\| 26 \| UINT32 \| 1 \| tokenizer.ggml.eos_token_id \| 128001 \|
	\| 27 \| UINT32 \| 1 \| tokenizer.ggml.padding_token_id \| 128001 \|
	\| 28 \| BOOL \| 1 \| tokenizer.ggml.add_bos_token \| True \|
	\| 29 \| BOOL \| 1 \| tokenizer.ggml.add_eos_token \| False \|
	\| 30 \| STRING \| 1 \| tokenizer.chat_template \| `{% if not add_generation_promp`...`{{'<｜Assistant｜>'}}{% endif %}` \|
	\| 31 \| UINT32 \| 1 \| general.quantization_version \| 2 \|
	\| 32 \| UINT32 \| 1 \| general.file_type \| 13 \|
	\| 33 \| STRING \| 1 \| quantize.imatrix.file \| `./imatrix/imatrix-DeepSeek-R1-Distill-Llama-8B-small.dat` \|
	\| 34 \| STRING \| 1 \| quantize.imatrix.dataset \| `../../datasets/imatrix/calibration_all_small.txt` \|
	\| 35 \| INT32 \| 1 \| quantize.imatrix.entries_count \| 225 \|
	\| 36 \| INT32 \| 1 \| quantize.imatrix.chunks_count \| 1130 \|

	## Tensors Overview ~8B Elements

	Total number of elements in all tensors: 8030261312 Elements

	- [DeepSeek-R1-Distill-Llama-8B-Q3\_K\_L.gguf - GGUF Internal File Dump](#deepseek-r1-distill-llama-8b-q3_k_lgguf---gguf-internal-file-dump)
	- [Key Value Metadata Store](#key-value-metadata-store)
	- [Tensors Overview ~8B Elements](#tensors-overview-8b-elements)
	- [Tensor Data Offset](#tensor-data-offset)
	- [Base Tensor Group : ~1B Elements](#base-tensor-group--1b-elements)
	- [Block 0 Tensor Group : ~218M Elements](#block-0-tensor-group--218m-elements)
	- [Block 1 Tensor Group : ~218M Elements](#block-1-tensor-group--218m-elements)
	- [Block 2 Tensor Group : ~218M Elements](#block-2-tensor-group--218m-elements)
	- [Block 3 Tensor Group : ~218M Elements](#block-3-tensor-group--218m-elements)
	- [Block 4 Tensor Group : ~218M Elements](#block-4-tensor-group--218m-elements)
	- [Block 5 Tensor Group : ~218M Elements](#block-5-tensor-group--218m-elements)
	- [Block 6 Tensor Group : ~218M Elements](#block-6-tensor-group--218m-elements)
	- [Block 7 Tensor Group : ~218M Elements](#block-7-tensor-group--218m-elements)
	- [Block 8 Tensor Group : ~218M Elements](#block-8-tensor-group--218m-elements)
	- [Block 9 Tensor Group : ~218M Elements](#block-9-tensor-group--218m-elements)
	- [Block 10 Tensor Group : ~218M Elements](#block-10-tensor-group--218m-elements)
	- [Block 11 Tensor Group : ~218M Elements](#block-11-tensor-group--218m-elements)
	- [Block 12 Tensor Group : ~218M Elements](#block-12-tensor-group--218m-elements)
	- [Block 13 Tensor Group : ~218M Elements](#block-13-tensor-group--218m-elements)
	- [Block 14 Tensor Group : ~218M Elements](#block-14-tensor-group--218m-elements)
	- [Block 15 Tensor Group : ~218M Elements](#block-15-tensor-group--218m-elements)
	- [Block 16 Tensor Group : ~218M Elements](#block-16-tensor-group--218m-elements)
	- [Block 17 Tensor Group : ~218M Elements](#block-17-tensor-group--218m-elements)
	- [Block 18 Tensor Group : ~218M Elements](#block-18-tensor-group--218m-elements)
	- [Block 19 Tensor Group : ~218M Elements](#block-19-tensor-group--218m-elements)
	- [Block 20 Tensor Group : ~218M Elements](#block-20-tensor-group--218m-elements)
	- [Block 21 Tensor Group : ~218M Elements](#block-21-tensor-group--218m-elements)
	- [Block 22 Tensor Group : ~218M Elements](#block-22-tensor-group--218m-elements)
	- [Block 23 Tensor Group : ~218M Elements](#block-23-tensor-group--218m-elements)
	- [Block 24 Tensor Group : ~218M Elements](#block-24-tensor-group--218m-elements)
	- [Block 25 Tensor Group : ~218M Elements](#block-25-tensor-group--218m-elements)
	- [Block 26 Tensor Group : ~218M Elements](#block-26-tensor-group--218m-elements)
	- [Block 27 Tensor Group : ~218M Elements](#block-27-tensor-group--218m-elements)
	- [Block 28 Tensor Group : ~218M Elements](#block-28-tensor-group--218m-elements)
	- [Block 29 Tensor Group : ~218M Elements](#block-29-tensor-group--218m-elements)
	- [Block 30 Tensor Group : ~218M Elements](#block-30-tensor-group--218m-elements)
	- [Block 31 Tensor Group : ~218M Elements](#block-31-tensor-group--218m-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 \| 0x779a80 \| 0xd746000 \|
	\| 1 \| output_norm.weight \| 0xdebfa80 \| 0x4000 \|
	\| 2 \| rope_freqs.weight \| 0xdec3a80 \| 0x100 \|
	\| 3 \| token_embd.weight \| 0xdec3b80 \| 0xd746000 \|
	\| 4 \| blk.0.attn_k.weight \| 0x1b609b80 \| 0x1b8000 \|
	\| 5 \| blk.0.attn_norm.weight \| 0x1b7c1b80 \| 0x4000 \|
	\| 6 \| blk.0.attn_output.weight \| 0x1b7c5b80 \| 0xb00000 \|
	\| 7 \| blk.0.attn_q.weight \| 0x1c2c5b80 \| 0x6e0000 \|
	\| 8 \| blk.0.attn_v.weight \| 0x1c9a5b80 \| 0x240000 \|
	\| 9 \| blk.0.ffn_down.weight \| 0x1cbe5b80 \| 0x2680000 \|
	\| 10 \| blk.0.ffn_gate.weight \| 0x1f265b80 \| 0x1810000 \|
	\| 11 \| blk.0.ffn_norm.weight \| 0x20a75b80 \| 0x4000 \|
	\| 12 \| blk.0.ffn_up.weight \| 0x20a79b80 \| 0x1810000 \|
	\| 13 \| blk.1.attn_k.weight \| 0x22289b80 \| 0x1b8000 \|
	\| 14 \| blk.1.attn_norm.weight \| 0x22441b80 \| 0x4000 \|
	\| 15 \| blk.1.attn_output.weight \| 0x22445b80 \| 0xb00000 \|
	\| 16 \| blk.1.attn_q.weight \| 0x22f45b80 \| 0x6e0000 \|
	\| 17 \| blk.1.attn_v.weight \| 0x23625b80 \| 0x240000 \|
	\| 18 \| blk.1.ffn_down.weight \| 0x23865b80 \| 0x2680000 \|
	\| 19 \| blk.1.ffn_gate.weight \| 0x25ee5b80 \| 0x1810000 \|
	\| 20 \| blk.1.ffn_norm.weight \| 0x276f5b80 \| 0x4000 \|
	\| 21 \| blk.1.ffn_up.weight \| 0x276f9b80 \| 0x1810000 \|
	\| 22 \| blk.2.attn_k.weight \| 0x28f09b80 \| 0x1b8000 \|
	\| 23 \| blk.2.attn_norm.weight \| 0x290c1b80 \| 0x4000 \|
	\| 24 \| blk.2.attn_output.weight \| 0x290c5b80 \| 0xb00000 \|
	\| 25 \| blk.2.attn_q.weight \| 0x29bc5b80 \| 0x6e0000 \|
	\| 26 \| blk.2.attn_v.weight \| 0x2a2a5b80 \| 0x240000 \|
	\| 27 \| blk.2.ffn_down.weight \| 0x2a4e5b80 \| 0x2680000 \|
	\| 28 \| blk.2.ffn_gate.weight \| 0x2cb65b80 \| 0x1810000 \|
	\| 29 \| blk.2.ffn_norm.weight \| 0x2e375b80 \| 0x4000 \|
	\| 30 \| blk.2.ffn_up.weight \| 0x2e379b80 \| 0x1810000 \|
	\| 31 \| blk.3.attn_k.weight \| 0x2fb89b80 \| 0x1b8000 \|
	\| 32 \| blk.3.attn_norm.weight \| 0x2fd41b80 \| 0x4000 \|
	\| 33 \| blk.3.attn_output.weight \| 0x2fd45b80 \| 0xb00000 \|
	\| 34 \| blk.3.attn_q.weight \| 0x30845b80 \| 0x6e0000 \|
	\| 35 \| blk.3.attn_v.weight \| 0x30f25b80 \| 0x240000 \|
	\| 36 \| blk.3.ffn_down.weight \| 0x31165b80 \| 0x2680000 \|
	\| 37 \| blk.3.ffn_gate.weight \| 0x337e5b80 \| 0x1810000 \|
	\| 38 \| blk.3.ffn_norm.weight \| 0x34ff5b80 \| 0x4000 \|
	\| 39 \| blk.3.ffn_up.weight \| 0x34ff9b80 \| 0x1810000 \|
	\| 40 \| blk.4.attn_k.weight \| 0x36809b80 \| 0x1b8000 \|
	\| 41 \| blk.4.attn_norm.weight \| 0x369c1b80 \| 0x4000 \|
	\| 42 \| blk.4.attn_output.weight \| 0x369c5b80 \| 0xb00000 \|
	\| 43 \| blk.4.attn_q.weight \| 0x374c5b80 \| 0x6e0000 \|
	\| 44 \| blk.4.attn_v.weight \| 0x37ba5b80 \| 0x240000 \|
	\| 45 \| blk.4.ffn_down.weight \| 0x37de5b80 \| 0x2680000 \|
	\| 46 \| blk.4.ffn_gate.weight \| 0x3a465b80 \| 0x1810000 \|
	\| 47 \| blk.4.ffn_norm.weight \| 0x3bc75b80 \| 0x4000 \|
	\| 48 \| blk.4.ffn_up.weight \| 0x3bc79b80 \| 0x1810000 \|
	\| 49 \| blk.5.attn_k.weight \| 0x3d489b80 \| 0x1b8000 \|
	\| 50 \| blk.5.attn_norm.weight \| 0x3d641b80 \| 0x4000 \|
	\| 51 \| blk.5.attn_output.weight \| 0x3d645b80 \| 0xb00000 \|
	\| 52 \| blk.5.attn_q.weight \| 0x3e145b80 \| 0x6e0000 \|
	\| 53 \| blk.5.attn_v.weight \| 0x3e825b80 \| 0x240000 \|
	\| 54 \| blk.5.ffn_down.weight \| 0x3ea65b80 \| 0x2680000 \|
	\| 55 \| blk.5.ffn_gate.weight \| 0x410e5b80 \| 0x1810000 \|
	\| 56 \| blk.5.ffn_norm.weight \| 0x428f5b80 \| 0x4000 \|
	\| 57 \| blk.5.ffn_up.weight \| 0x428f9b80 \| 0x1810000 \|
	\| 58 \| blk.6.attn_k.weight \| 0x44109b80 \| 0x1b8000 \|
	\| 59 \| blk.6.attn_norm.weight \| 0x442c1b80 \| 0x4000 \|
	\| 60 \| blk.6.attn_output.weight \| 0x442c5b80 \| 0xb00000 \|
	\| 61 \| blk.6.attn_q.weight \| 0x44dc5b80 \| 0x6e0000 \|
	\| 62 \| blk.6.attn_v.weight \| 0x454a5b80 \| 0x240000 \|
	\| 63 \| blk.6.ffn_down.weight \| 0x456e5b80 \| 0x2680000 \|
	\| 64 \| blk.6.ffn_gate.weight \| 0x47d65b80 \| 0x1810000 \|
	\| 65 \| blk.6.ffn_norm.weight \| 0x49575b80 \| 0x4000 \|
	\| 66 \| blk.6.ffn_up.weight \| 0x49579b80 \| 0x1810000 \|
	\| 67 \| blk.7.attn_k.weight \| 0x4ad89b80 \| 0x1b8000 \|
	\| 68 \| blk.7.attn_norm.weight \| 0x4af41b80 \| 0x4000 \|
	\| 69 \| blk.7.attn_output.weight \| 0x4af45b80 \| 0xb00000 \|
	\| 70 \| blk.7.attn_q.weight \| 0x4ba45b80 \| 0x6e0000 \|
	\| 71 \| blk.7.attn_v.weight \| 0x4c125b80 \| 0x240000 \|
	\| 72 \| blk.7.ffn_down.weight \| 0x4c365b80 \| 0x2680000 \|
	\| 73 \| blk.7.ffn_gate.weight \| 0x4e9e5b80 \| 0x1810000 \|
	\| 74 \| blk.7.ffn_norm.weight \| 0x501f5b80 \| 0x4000 \|
	\| 75 \| blk.7.ffn_up.weight \| 0x501f9b80 \| 0x1810000 \|
	\| 76 \| blk.8.attn_k.weight \| 0x51a09b80 \| 0x1b8000 \|
	\| 77 \| blk.8.attn_norm.weight \| 0x51bc1b80 \| 0x4000 \|
	\| 78 \| blk.8.attn_output.weight \| 0x51bc5b80 \| 0xb00000 \|
	\| 79 \| blk.8.attn_q.weight \| 0x526c5b80 \| 0x6e0000 \|
	\| 80 \| blk.8.attn_v.weight \| 0x52da5b80 \| 0x240000 \|
	\| 81 \| blk.8.ffn_down.weight \| 0x52fe5b80 \| 0x2680000 \|
	\| 82 \| blk.8.ffn_gate.weight \| 0x55665b80 \| 0x1810000 \|
	\| 83 \| blk.8.ffn_norm.weight \| 0x56e75b80 \| 0x4000 \|
	\| 84 \| blk.8.ffn_up.weight \| 0x56e79b80 \| 0x1810000 \|
	\| 85 \| blk.9.attn_k.weight \| 0x58689b80 \| 0x1b8000 \|
	\| 86 \| blk.9.attn_norm.weight \| 0x58841b80 \| 0x4000 \|
	\| 87 \| blk.9.attn_output.weight \| 0x58845b80 \| 0xb00000 \|
	\| 88 \| blk.9.attn_q.weight \| 0x59345b80 \| 0x6e0000 \|
	\| 89 \| blk.9.attn_v.weight \| 0x59a25b80 \| 0x240000 \|
	\| 90 \| blk.9.ffn_down.weight \| 0x59c65b80 \| 0x2680000 \|
	\| 91 \| blk.9.ffn_gate.weight \| 0x5c2e5b80 \| 0x1810000 \|
	\| 92 \| blk.9.ffn_norm.weight \| 0x5daf5b80 \| 0x4000 \|
	\| 93 \| blk.9.ffn_up.weight \| 0x5daf9b80 \| 0x1810000 \|
	\| 94 \| blk.10.attn_k.weight \| 0x5f309b80 \| 0x1b8000 \|
	\| 95 \| blk.10.attn_norm.weight \| 0x5f4c1b80 \| 0x4000 \|
	\| 96 \| blk.10.attn_output.weight \| 0x5f4c5b80 \| 0xb00000 \|
	\| 97 \| blk.10.attn_q.weight \| 0x5ffc5b80 \| 0x6e0000 \|
	\| 98 \| blk.10.attn_v.weight \| 0x606a5b80 \| 0x240000 \|
	\| 99 \| blk.10.ffn_down.weight \| 0x608e5b80 \| 0x2680000 \|
	\| 100 \| blk.10.ffn_gate.weight \| 0x62f65b80 \| 0x1810000 \|
	\| 101 \| blk.10.ffn_norm.weight \| 0x64775b80 \| 0x4000 \|
	\| 102 \| blk.10.ffn_up.weight \| 0x64779b80 \| 0x1810000 \|
	\| 103 \| blk.11.attn_k.weight \| 0x65f89b80 \| 0x1b8000 \|
	\| 104 \| blk.11.attn_norm.weight \| 0x66141b80 \| 0x4000 \|
	\| 105 \| blk.11.attn_output.weight \| 0x66145b80 \| 0xb00000 \|
	\| 106 \| blk.11.attn_q.weight \| 0x66c45b80 \| 0x6e0000 \|
	\| 107 \| blk.11.attn_v.weight \| 0x67325b80 \| 0x240000 \|
	\| 108 \| blk.11.ffn_down.weight \| 0x67565b80 \| 0x2680000 \|
	\| 109 \| blk.11.ffn_gate.weight \| 0x69be5b80 \| 0x1810000 \|
	\| 110 \| blk.11.ffn_norm.weight \| 0x6b3f5b80 \| 0x4000 \|
	\| 111 \| blk.11.ffn_up.weight \| 0x6b3f9b80 \| 0x1810000 \|
	\| 112 \| blk.12.attn_k.weight \| 0x6cc09b80 \| 0x1b8000 \|
	\| 113 \| blk.12.attn_norm.weight \| 0x6cdc1b80 \| 0x4000 \|
	\| 114 \| blk.12.attn_output.weight \| 0x6cdc5b80 \| 0xb00000 \|
	\| 115 \| blk.12.attn_q.weight \| 0x6d8c5b80 \| 0x6e0000 \|
	\| 116 \| blk.12.attn_v.weight \| 0x6dfa5b80 \| 0x240000 \|
	\| 117 \| blk.12.ffn_down.weight \| 0x6e1e5b80 \| 0x2680000 \|
	\| 118 \| blk.12.ffn_gate.weight \| 0x70865b80 \| 0x1810000 \|
	\| 119 \| blk.12.ffn_norm.weight \| 0x72075b80 \| 0x4000 \|
	\| 120 \| blk.12.ffn_up.weight \| 0x72079b80 \| 0x1810000 \|
	\| 121 \| blk.13.attn_k.weight \| 0x73889b80 \| 0x1b8000 \|
	\| 122 \| blk.13.attn_norm.weight \| 0x73a41b80 \| 0x4000 \|
	\| 123 \| blk.13.attn_output.weight \| 0x73a45b80 \| 0xb00000 \|
	\| 124 \| blk.13.attn_q.weight \| 0x74545b80 \| 0x6e0000 \|
	\| 125 \| blk.13.attn_v.weight \| 0x74c25b80 \| 0x240000 \|
	\| 126 \| blk.13.ffn_down.weight \| 0x74e65b80 \| 0x2680000 \|
	\| 127 \| blk.13.ffn_gate.weight \| 0x774e5b80 \| 0x1810000 \|
	\| 128 \| blk.13.ffn_norm.weight \| 0x78cf5b80 \| 0x4000 \|
	\| 129 \| blk.13.ffn_up.weight \| 0x78cf9b80 \| 0x1810000 \|
	\| 130 \| blk.14.attn_k.weight \| 0x7a509b80 \| 0x1b8000 \|
	\| 131 \| blk.14.attn_norm.weight \| 0x7a6c1b80 \| 0x4000 \|
	\| 132 \| blk.14.attn_output.weight \| 0x7a6c5b80 \| 0xb00000 \|
	\| 133 \| blk.14.attn_q.weight \| 0x7b1c5b80 \| 0x6e0000 \|
	\| 134 \| blk.14.attn_v.weight \| 0x7b8a5b80 \| 0x240000 \|
	\| 135 \| blk.14.ffn_down.weight \| 0x7bae5b80 \| 0x2680000 \|
	\| 136 \| blk.14.ffn_gate.weight \| 0x7e165b80 \| 0x1810000 \|
	\| 137 \| blk.14.ffn_norm.weight \| 0x7f975b80 \| 0x4000 \|
	\| 138 \| blk.14.ffn_up.weight \| 0x7f979b80 \| 0x1810000 \|
	\| 139 \| blk.15.attn_k.weight \| 0x81189b80 \| 0x1b8000 \|
	\| 140 \| blk.15.attn_norm.weight \| 0x81341b80 \| 0x4000 \|
	\| 141 \| blk.15.attn_output.weight \| 0x81345b80 \| 0xb00000 \|
	\| 142 \| blk.15.attn_q.weight \| 0x81e45b80 \| 0x6e0000 \|
	\| 143 \| blk.15.attn_v.weight \| 0x82525b80 \| 0x240000 \|
	\| 144 \| blk.15.ffn_down.weight \| 0x82765b80 \| 0x2680000 \|
	\| 145 \| blk.15.ffn_gate.weight \| 0x84de5b80 \| 0x1810000 \|
	\| 146 \| blk.15.ffn_norm.weight \| 0x865f5b80 \| 0x4000 \|
	\| 147 \| blk.15.ffn_up.weight \| 0x865f9b80 \| 0x1810000 \|
	\| 148 \| blk.16.attn_k.weight \| 0x87e09b80 \| 0x150000 \|
	\| 149 \| blk.16.attn_norm.weight \| 0x87f59b80 \| 0x4000 \|
	\| 150 \| blk.16.attn_output.weight \| 0x87f5db80 \| 0xb00000 \|
	\| 151 \| blk.16.attn_q.weight \| 0x88a5db80 \| 0x540000 \|
	\| 152 \| blk.16.attn_v.weight \| 0x88f9db80 \| 0x240000 \|
	\| 153 \| blk.16.ffn_down.weight \| 0x891ddb80 \| 0x1f80000 \|
	\| 154 \| blk.16.ffn_gate.weight \| 0x8b15db80 \| 0x1260000 \|
	\| 155 \| blk.16.ffn_norm.weight \| 0x8c3bdb80 \| 0x4000 \|
	\| 156 \| blk.16.ffn_up.weight \| 0x8c3c1b80 \| 0x1260000 \|
	\| 157 \| blk.17.attn_k.weight \| 0x8d621b80 \| 0x150000 \|
	\| 158 \| blk.17.attn_norm.weight \| 0x8d771b80 \| 0x4000 \|
	\| 159 \| blk.17.attn_output.weight \| 0x8d775b80 \| 0xb00000 \|
	\| 160 \| blk.17.attn_q.weight \| 0x8e275b80 \| 0x540000 \|
	\| 161 \| blk.17.attn_v.weight \| 0x8e7b5b80 \| 0x240000 \|
	\| 162 \| blk.17.ffn_down.weight \| 0x8e9f5b80 \| 0x1f80000 \|
	\| 163 \| blk.17.ffn_gate.weight \| 0x90975b80 \| 0x1260000 \|
	\| 164 \| blk.17.ffn_norm.weight \| 0x91bd5b80 \| 0x4000 \|
	\| 165 \| blk.17.ffn_up.weight \| 0x91bd9b80 \| 0x1260000 \|
	\| 166 \| blk.18.attn_k.weight \| 0x92e39b80 \| 0x150000 \|
	\| 167 \| blk.18.attn_norm.weight \| 0x92f89b80 \| 0x4000 \|
	\| 168 \| blk.18.attn_output.weight \| 0x92f8db80 \| 0xb00000 \|
	\| 169 \| blk.18.attn_q.weight \| 0x93a8db80 \| 0x540000 \|
	\| 170 \| blk.18.attn_v.weight \| 0x93fcdb80 \| 0x240000 \|
	\| 171 \| blk.18.ffn_down.weight \| 0x9420db80 \| 0x1f80000 \|
	\| 172 \| blk.18.ffn_gate.weight \| 0x9618db80 \| 0x1260000 \|
	\| 173 \| blk.18.ffn_norm.weight \| 0x973edb80 \| 0x4000 \|
	\| 174 \| blk.18.ffn_up.weight \| 0x973f1b80 \| 0x1260000 \|
	\| 175 \| blk.19.attn_k.weight \| 0x98651b80 \| 0x150000 \|
	\| 176 \| blk.19.attn_norm.weight \| 0x987a1b80 \| 0x4000 \|
	\| 177 \| blk.19.attn_output.weight \| 0x987a5b80 \| 0xb00000 \|
	\| 178 \| blk.19.attn_q.weight \| 0x992a5b80 \| 0x540000 \|
	\| 179 \| blk.19.attn_v.weight \| 0x997e5b80 \| 0x240000 \|
	\| 180 \| blk.19.ffn_down.weight \| 0x99a25b80 \| 0x1f80000 \|
	\| 181 \| blk.19.ffn_gate.weight \| 0x9b9a5b80 \| 0x1260000 \|
	\| 182 \| blk.19.ffn_norm.weight \| 0x9cc05b80 \| 0x4000 \|
	\| 183 \| blk.19.ffn_up.weight \| 0x9cc09b80 \| 0x1260000 \|
	\| 184 \| blk.20.attn_k.weight \| 0x9de69b80 \| 0x150000 \|
	\| 185 \| blk.20.attn_norm.weight \| 0x9dfb9b80 \| 0x4000 \|
	\| 186 \| blk.20.attn_output.weight \| 0x9dfbdb80 \| 0xb00000 \|
	\| 187 \| blk.20.attn_q.weight \| 0x9eabdb80 \| 0x540000 \|
	\| 188 \| blk.20.attn_v.weight \| 0x9effdb80 \| 0x240000 \|
	\| 189 \| blk.20.ffn_down.weight \| 0x9f23db80 \| 0x1f80000 \|
	\| 190 \| blk.20.ffn_gate.weight \| 0xa11bdb80 \| 0x1260000 \|
	\| 191 \| blk.20.ffn_norm.weight \| 0xa241db80 \| 0x4000 \|
	\| 192 \| blk.20.ffn_up.weight \| 0xa2421b80 \| 0x1260000 \|
	\| 193 \| blk.21.attn_k.weight \| 0xa3681b80 \| 0x150000 \|
	\| 194 \| blk.21.attn_norm.weight \| 0xa37d1b80 \| 0x4000 \|
	\| 195 \| blk.21.attn_output.weight \| 0xa37d5b80 \| 0xb00000 \|
	\| 196 \| blk.21.attn_q.weight \| 0xa42d5b80 \| 0x540000 \|
	\| 197 \| blk.21.attn_v.weight \| 0xa4815b80 \| 0x240000 \|
	\| 198 \| blk.21.ffn_down.weight \| 0xa4a55b80 \| 0x1f80000 \|
	\| 199 \| blk.21.ffn_gate.weight \| 0xa69d5b80 \| 0x1260000 \|
	\| 200 \| blk.21.ffn_norm.weight \| 0xa7c35b80 \| 0x4000 \|
	\| 201 \| blk.21.ffn_up.weight \| 0xa7c39b80 \| 0x1260000 \|
	\| 202 \| blk.22.attn_k.weight \| 0xa8e99b80 \| 0x150000 \|
	\| 203 \| blk.22.attn_norm.weight \| 0xa8fe9b80 \| 0x4000 \|
	\| 204 \| blk.22.attn_output.weight \| 0xa8fedb80 \| 0xb00000 \|
	\| 205 \| blk.22.attn_q.weight \| 0xa9aedb80 \| 0x540000 \|
	\| 206 \| blk.22.attn_v.weight \| 0xaa02db80 \| 0x240000 \|
	\| 207 \| blk.22.ffn_down.weight \| 0xaa26db80 \| 0x1f80000 \|
	\| 208 \| blk.22.ffn_gate.weight \| 0xac1edb80 \| 0x1260000 \|
	\| 209 \| blk.22.ffn_norm.weight \| 0xad44db80 \| 0x4000 \|
	\| 210 \| blk.22.ffn_up.weight \| 0xad451b80 \| 0x1260000 \|
	\| 211 \| blk.23.attn_k.weight \| 0xae6b1b80 \| 0x150000 \|
	\| 212 \| blk.23.attn_norm.weight \| 0xae801b80 \| 0x4000 \|
	\| 213 \| blk.23.attn_output.weight \| 0xae805b80 \| 0xb00000 \|
	\| 214 \| blk.23.attn_q.weight \| 0xaf305b80 \| 0x540000 \|
	\| 215 \| blk.23.attn_v.weight \| 0xaf845b80 \| 0x240000 \|
	\| 216 \| blk.23.ffn_down.weight \| 0xafa85b80 \| 0x1f80000 \|
	\| 217 \| blk.23.ffn_gate.weight \| 0xb1a05b80 \| 0x1260000 \|
	\| 218 \| blk.23.ffn_norm.weight \| 0xb2c65b80 \| 0x4000 \|
	\| 219 \| blk.23.ffn_up.weight \| 0xb2c69b80 \| 0x1260000 \|
	\| 220 \| blk.24.attn_k.weight \| 0xb3ec9b80 \| 0x150000 \|
	\| 221 \| blk.24.attn_norm.weight \| 0xb4019b80 \| 0x4000 \|
	\| 222 \| blk.24.attn_output.weight \| 0xb401db80 \| 0xb00000 \|
	\| 223 \| blk.24.attn_q.weight \| 0xb4b1db80 \| 0x540000 \|
	\| 224 \| blk.24.attn_v.weight \| 0xb505db80 \| 0x240000 \|
	\| 225 \| blk.24.ffn_down.weight \| 0xb529db80 \| 0x1f80000 \|
	\| 226 \| blk.24.ffn_gate.weight \| 0xb721db80 \| 0x1260000 \|
	\| 227 \| blk.24.ffn_norm.weight \| 0xb847db80 \| 0x4000 \|
	\| 228 \| blk.24.ffn_up.weight \| 0xb8481b80 \| 0x1260000 \|
	\| 229 \| blk.25.attn_k.weight \| 0xb96e1b80 \| 0x150000 \|
	\| 230 \| blk.25.attn_norm.weight \| 0xb9831b80 \| 0x4000 \|
	\| 231 \| blk.25.attn_output.weight \| 0xb9835b80 \| 0xb00000 \|
	\| 232 \| blk.25.attn_q.weight \| 0xba335b80 \| 0x540000 \|
	\| 233 \| blk.25.attn_v.weight \| 0xba875b80 \| 0x240000 \|
	\| 234 \| blk.25.ffn_down.weight \| 0xbaab5b80 \| 0x1f80000 \|
	\| 235 \| blk.25.ffn_gate.weight \| 0xbca35b80 \| 0x1260000 \|
	\| 236 \| blk.25.ffn_norm.weight \| 0xbdc95b80 \| 0x4000 \|
	\| 237 \| blk.25.ffn_up.weight \| 0xbdc99b80 \| 0x1260000 \|
	\| 238 \| blk.26.attn_k.weight \| 0xbeef9b80 \| 0x150000 \|
	\| 239 \| blk.26.attn_norm.weight \| 0xbf049b80 \| 0x4000 \|
	\| 240 \| blk.26.attn_output.weight \| 0xbf04db80 \| 0xb00000 \|
	\| 241 \| blk.26.attn_q.weight \| 0xbfb4db80 \| 0x540000 \|
	\| 242 \| blk.26.attn_v.weight \| 0xc008db80 \| 0x240000 \|
	\| 243 \| blk.26.ffn_down.weight \| 0xc02cdb80 \| 0x1f80000 \|
	\| 244 \| blk.26.ffn_gate.weight \| 0xc224db80 \| 0x1260000 \|
	\| 245 \| blk.26.ffn_norm.weight \| 0xc34adb80 \| 0x4000 \|
	\| 246 \| blk.26.ffn_up.weight \| 0xc34b1b80 \| 0x1260000 \|
	\| 247 \| blk.27.attn_k.weight \| 0xc4711b80 \| 0x150000 \|
	\| 248 \| blk.27.attn_norm.weight \| 0xc4861b80 \| 0x4000 \|
	\| 249 \| blk.27.attn_output.weight \| 0xc4865b80 \| 0xb00000 \|
	\| 250 \| blk.27.attn_q.weight \| 0xc5365b80 \| 0x540000 \|
	\| 251 \| blk.27.attn_v.weight \| 0xc58a5b80 \| 0x240000 \|
	\| 252 \| blk.27.ffn_down.weight \| 0xc5ae5b80 \| 0x1f80000 \|
	\| 253 \| blk.27.ffn_gate.weight \| 0xc7a65b80 \| 0x1260000 \|
	\| 254 \| blk.27.ffn_norm.weight \| 0xc8cc5b80 \| 0x4000 \|
	\| 255 \| blk.27.ffn_up.weight \| 0xc8cc9b80 \| 0x1260000 \|
	\| 256 \| blk.28.attn_k.weight \| 0xc9f29b80 \| 0x150000 \|
	\| 257 \| blk.28.attn_norm.weight \| 0xca079b80 \| 0x4000 \|
	\| 258 \| blk.28.attn_output.weight \| 0xca07db80 \| 0xb00000 \|
	\| 259 \| blk.28.attn_q.weight \| 0xcab7db80 \| 0x540000 \|
	\| 260 \| blk.28.attn_v.weight \| 0xcb0bdb80 \| 0x240000 \|
	\| 261 \| blk.28.ffn_down.weight \| 0xcb2fdb80 \| 0x1f80000 \|
	\| 262 \| blk.28.ffn_gate.weight \| 0xcd27db80 \| 0x1260000 \|
	\| 263 \| blk.28.ffn_norm.weight \| 0xce4ddb80 \| 0x4000 \|
	\| 264 \| blk.28.ffn_up.weight \| 0xce4e1b80 \| 0x1260000 \|
	\| 265 \| blk.29.attn_k.weight \| 0xcf741b80 \| 0x150000 \|
	\| 266 \| blk.29.attn_norm.weight \| 0xcf891b80 \| 0x4000 \|
	\| 267 \| blk.29.attn_output.weight \| 0xcf895b80 \| 0xb00000 \|
	\| 268 \| blk.29.attn_q.weight \| 0xd0395b80 \| 0x540000 \|
	\| 269 \| blk.29.attn_v.weight \| 0xd08d5b80 \| 0x240000 \|
	\| 270 \| blk.29.ffn_down.weight \| 0xd0b15b80 \| 0x1f80000 \|
	\| 271 \| blk.29.ffn_gate.weight \| 0xd2a95b80 \| 0x1260000 \|
	\| 272 \| blk.29.ffn_norm.weight \| 0xd3cf5b80 \| 0x4000 \|
	\| 273 \| blk.29.ffn_up.weight \| 0xd3cf9b80 \| 0x1260000 \|
	\| 274 \| blk.30.attn_k.weight \| 0xd4f59b80 \| 0x150000 \|
	\| 275 \| blk.30.attn_norm.weight \| 0xd50a9b80 \| 0x4000 \|
	\| 276 \| blk.30.attn_output.weight \| 0xd50adb80 \| 0xb00000 \|
	\| 277 \| blk.30.attn_q.weight \| 0xd5badb80 \| 0x540000 \|
	\| 278 \| blk.30.attn_v.weight \| 0xd60edb80 \| 0x240000 \|
	\| 279 \| blk.30.ffn_down.weight \| 0xd632db80 \| 0x1f80000 \|
	\| 280 \| blk.30.ffn_gate.weight \| 0xd82adb80 \| 0x1260000 \|
	\| 281 \| blk.30.ffn_norm.weight \| 0xd950db80 \| 0x4000 \|
	\| 282 \| blk.30.ffn_up.weight \| 0xd9511b80 \| 0x1260000 \|
	\| 283 \| blk.31.attn_k.weight \| 0xda771b80 \| 0x150000 \|
	\| 284 \| blk.31.attn_norm.weight \| 0xda8c1b80 \| 0x4000 \|
	\| 285 \| blk.31.attn_output.weight \| 0xda8c5b80 \| 0xb00000 \|
	\| 286 \| blk.31.attn_q.weight \| 0xdb3c5b80 \| 0x540000 \|
	\| 287 \| blk.31.attn_v.weight \| 0xdb905b80 \| 0x240000 \|
	\| 288 \| blk.31.ffn_down.weight \| 0xdbb45b80 \| 0x1f80000 \|
	\| 289 \| blk.31.ffn_gate.weight \| 0xddac5b80 \| 0x1260000 \|
	\| 290 \| blk.31.ffn_norm.weight \| 0xded25b80 \| 0x4000 \|
	\| 291 \| blk.31.ffn_up.weight \| 0xded29b80 \| 0x1260000 \|

	### <a name="base">Base Tensor Group : ~1B Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------- \| :------------------------------- \| :---------------- \| :-------------------- \| :--- \|
	\| 0 \| output.weight \| Output (W) \| (~525M) 525336576 \| 4096 x 128256 x 1 x 1 \| Q3_K \|
	\| 1 \| output_norm.weight \| Output Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 2 \| rope_freqs.weight \| Rope_Freqs (W) \| ( 64) 64 \| 64 x 1 x 1 x 1 \| F32 \|
	\| 3 \| token_embd.weight \| Token Embedding (W) \| (~525M) 525336576 \| 4096 x 128256 x 1 x 1 \| Q3_K \|

	- Total elements in base: ( ~1B) 1050677312
	- Percentage of total elements: 13.08%


	### <a name="blk_0">Block 0 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 4 \| blk.0.attn_k.weight \| Block 0 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 5 \| blk.0.attn_norm.weight \| Block 0 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 6 \| blk.0.attn_output.weight \| Block 0 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 7 \| blk.0.attn_q.weight \| Block 0 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 8 \| blk.0.attn_v.weight \| Block 0 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 9 \| blk.0.ffn_down.weight \| Block 0 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 10 \| blk.0.ffn_gate.weight \| Block 0 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 11 \| blk.0.ffn_norm.weight \| Block 0 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 12 \| blk.0.ffn_up.weight \| Block 0 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.0: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_1">Block 1 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 13 \| blk.1.attn_k.weight \| Block 1 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 14 \| blk.1.attn_norm.weight \| Block 1 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 15 \| blk.1.attn_output.weight \| Block 1 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 16 \| blk.1.attn_q.weight \| Block 1 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 17 \| blk.1.attn_v.weight \| Block 1 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 18 \| blk.1.ffn_down.weight \| Block 1 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 19 \| blk.1.ffn_gate.weight \| Block 1 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 20 \| blk.1.ffn_norm.weight \| Block 1 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 21 \| blk.1.ffn_up.weight \| Block 1 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.1: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_2">Block 2 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 22 \| blk.2.attn_k.weight \| Block 2 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 23 \| blk.2.attn_norm.weight \| Block 2 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 24 \| blk.2.attn_output.weight \| Block 2 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 25 \| blk.2.attn_q.weight \| Block 2 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 26 \| blk.2.attn_v.weight \| Block 2 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 27 \| blk.2.ffn_down.weight \| Block 2 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 28 \| blk.2.ffn_gate.weight \| Block 2 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 29 \| blk.2.ffn_norm.weight \| Block 2 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 30 \| blk.2.ffn_up.weight \| Block 2 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.2: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_3">Block 3 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 31 \| blk.3.attn_k.weight \| Block 3 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 32 \| blk.3.attn_norm.weight \| Block 3 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 33 \| blk.3.attn_output.weight \| Block 3 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 34 \| blk.3.attn_q.weight \| Block 3 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 35 \| blk.3.attn_v.weight \| Block 3 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 36 \| blk.3.ffn_down.weight \| Block 3 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 37 \| blk.3.ffn_gate.weight \| Block 3 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 38 \| blk.3.ffn_norm.weight \| Block 3 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 39 \| blk.3.ffn_up.weight \| Block 3 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.3: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_4">Block 4 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 40 \| blk.4.attn_k.weight \| Block 4 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 41 \| blk.4.attn_norm.weight \| Block 4 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 42 \| blk.4.attn_output.weight \| Block 4 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 43 \| blk.4.attn_q.weight \| Block 4 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 44 \| blk.4.attn_v.weight \| Block 4 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 45 \| blk.4.ffn_down.weight \| Block 4 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 46 \| blk.4.ffn_gate.weight \| Block 4 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 47 \| blk.4.ffn_norm.weight \| Block 4 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 48 \| blk.4.ffn_up.weight \| Block 4 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.4: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_5">Block 5 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 49 \| blk.5.attn_k.weight \| Block 5 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 50 \| blk.5.attn_norm.weight \| Block 5 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 51 \| blk.5.attn_output.weight \| Block 5 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 52 \| blk.5.attn_q.weight \| Block 5 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 53 \| blk.5.attn_v.weight \| Block 5 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 54 \| blk.5.ffn_down.weight \| Block 5 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 55 \| blk.5.ffn_gate.weight \| Block 5 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 56 \| blk.5.ffn_norm.weight \| Block 5 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 57 \| blk.5.ffn_up.weight \| Block 5 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.5: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_6">Block 6 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 58 \| blk.6.attn_k.weight \| Block 6 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 59 \| blk.6.attn_norm.weight \| Block 6 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 60 \| blk.6.attn_output.weight \| Block 6 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 61 \| blk.6.attn_q.weight \| Block 6 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 62 \| blk.6.attn_v.weight \| Block 6 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 63 \| blk.6.ffn_down.weight \| Block 6 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 64 \| blk.6.ffn_gate.weight \| Block 6 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 65 \| blk.6.ffn_norm.weight \| Block 6 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 66 \| blk.6.ffn_up.weight \| Block 6 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.6: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_7">Block 7 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 67 \| blk.7.attn_k.weight \| Block 7 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 68 \| blk.7.attn_norm.weight \| Block 7 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 69 \| blk.7.attn_output.weight \| Block 7 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 70 \| blk.7.attn_q.weight \| Block 7 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 71 \| blk.7.attn_v.weight \| Block 7 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 72 \| blk.7.ffn_down.weight \| Block 7 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 73 \| blk.7.ffn_gate.weight \| Block 7 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 74 \| blk.7.ffn_norm.weight \| Block 7 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 75 \| blk.7.ffn_up.weight \| Block 7 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.7: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_8">Block 8 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 76 \| blk.8.attn_k.weight \| Block 8 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 77 \| blk.8.attn_norm.weight \| Block 8 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 78 \| blk.8.attn_output.weight \| Block 8 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 79 \| blk.8.attn_q.weight \| Block 8 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 80 \| blk.8.attn_v.weight \| Block 8 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 81 \| blk.8.ffn_down.weight \| Block 8 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 82 \| blk.8.ffn_gate.weight \| Block 8 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 83 \| blk.8.ffn_norm.weight \| Block 8 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 84 \| blk.8.ffn_up.weight \| Block 8 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.8: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_9">Block 9 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :----------------------- \| :--------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 85 \| blk.9.attn_k.weight \| Block 9 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 86 \| blk.9.attn_norm.weight \| Block 9 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 87 \| blk.9.attn_output.weight \| Block 9 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 88 \| blk.9.attn_q.weight \| Block 9 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 89 \| blk.9.attn_v.weight \| Block 9 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 90 \| blk.9.ffn_down.weight \| Block 9 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 91 \| blk.9.ffn_gate.weight \| Block 9 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 92 \| blk.9.ffn_norm.weight \| Block 9 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 93 \| blk.9.ffn_up.weight \| Block 9 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.9: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_10">Block 10 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 94 \| blk.10.attn_k.weight \| Block 10 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 95 \| blk.10.attn_norm.weight \| Block 10 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 96 \| blk.10.attn_output.weight \| Block 10 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 97 \| blk.10.attn_q.weight \| Block 10 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 98 \| blk.10.attn_v.weight \| Block 10 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 99 \| blk.10.ffn_down.weight \| Block 10 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 100 \| blk.10.ffn_gate.weight \| Block 10 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 101 \| blk.10.ffn_norm.weight \| Block 10 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 102 \| blk.10.ffn_up.weight \| Block 10 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.10: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_11">Block 11 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 103 \| blk.11.attn_k.weight \| Block 11 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 104 \| blk.11.attn_norm.weight \| Block 11 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 105 \| blk.11.attn_output.weight \| Block 11 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 106 \| blk.11.attn_q.weight \| Block 11 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 107 \| blk.11.attn_v.weight \| Block 11 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 108 \| blk.11.ffn_down.weight \| Block 11 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 109 \| blk.11.ffn_gate.weight \| Block 11 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 110 \| blk.11.ffn_norm.weight \| Block 11 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 111 \| blk.11.ffn_up.weight \| Block 11 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.11: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_12">Block 12 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 112 \| blk.12.attn_k.weight \| Block 12 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 113 \| blk.12.attn_norm.weight \| Block 12 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 114 \| blk.12.attn_output.weight \| Block 12 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 115 \| blk.12.attn_q.weight \| Block 12 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 116 \| blk.12.attn_v.weight \| Block 12 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 117 \| blk.12.ffn_down.weight \| Block 12 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 118 \| blk.12.ffn_gate.weight \| Block 12 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 119 \| blk.12.ffn_norm.weight \| Block 12 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 120 \| blk.12.ffn_up.weight \| Block 12 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.12: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_13">Block 13 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 121 \| blk.13.attn_k.weight \| Block 13 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 122 \| blk.13.attn_norm.weight \| Block 13 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 123 \| blk.13.attn_output.weight \| Block 13 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 124 \| blk.13.attn_q.weight \| Block 13 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 125 \| blk.13.attn_v.weight \| Block 13 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 126 \| blk.13.ffn_down.weight \| Block 13 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 127 \| blk.13.ffn_gate.weight \| Block 13 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 128 \| blk.13.ffn_norm.weight \| Block 13 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 129 \| blk.13.ffn_up.weight \| Block 13 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.13: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_14">Block 14 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 130 \| blk.14.attn_k.weight \| Block 14 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 131 \| blk.14.attn_norm.weight \| Block 14 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 132 \| blk.14.attn_output.weight \| Block 14 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 133 \| blk.14.attn_q.weight \| Block 14 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 134 \| blk.14.attn_v.weight \| Block 14 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 135 \| blk.14.ffn_down.weight \| Block 14 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 136 \| blk.14.ffn_gate.weight \| Block 14 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 137 \| blk.14.ffn_norm.weight \| Block 14 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 138 \| blk.14.ffn_up.weight \| Block 14 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.14: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_15">Block 15 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 139 \| blk.15.attn_k.weight \| Block 15 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q3_K \|
	\| 140 \| blk.15.attn_norm.weight \| Block 15 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 141 \| blk.15.attn_output.weight \| Block 15 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 142 \| blk.15.attn_q.weight \| Block 15 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q3_K \|
	\| 143 \| blk.15.attn_v.weight \| Block 15 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 144 \| blk.15.ffn_down.weight \| Block 15 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q5_K \|
	\| 145 \| blk.15.ffn_gate.weight \| Block 15 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|
	\| 146 \| blk.15.ffn_norm.weight \| Block 15 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 147 \| blk.15.ffn_up.weight \| Block 15 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q3_K \|

	- Total elements in blk.15: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_16">Block 16 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 148 \| blk.16.attn_k.weight \| Block 16 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 149 \| blk.16.attn_norm.weight \| Block 16 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 150 \| blk.16.attn_output.weight \| Block 16 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 151 \| blk.16.attn_q.weight \| Block 16 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 152 \| blk.16.attn_v.weight \| Block 16 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 153 \| blk.16.ffn_down.weight \| Block 16 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 154 \| blk.16.ffn_gate.weight \| Block 16 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 155 \| blk.16.ffn_norm.weight \| Block 16 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 156 \| blk.16.ffn_up.weight \| Block 16 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.16: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_17">Block 17 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 157 \| blk.17.attn_k.weight \| Block 17 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 158 \| blk.17.attn_norm.weight \| Block 17 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 159 \| blk.17.attn_output.weight \| Block 17 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 160 \| blk.17.attn_q.weight \| Block 17 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 161 \| blk.17.attn_v.weight \| Block 17 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 162 \| blk.17.ffn_down.weight \| Block 17 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 163 \| blk.17.ffn_gate.weight \| Block 17 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 164 \| blk.17.ffn_norm.weight \| Block 17 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 165 \| blk.17.ffn_up.weight \| Block 17 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.17: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_18">Block 18 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 166 \| blk.18.attn_k.weight \| Block 18 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 167 \| blk.18.attn_norm.weight \| Block 18 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 168 \| blk.18.attn_output.weight \| Block 18 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 169 \| blk.18.attn_q.weight \| Block 18 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 170 \| blk.18.attn_v.weight \| Block 18 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 171 \| blk.18.ffn_down.weight \| Block 18 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 172 \| blk.18.ffn_gate.weight \| Block 18 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 173 \| blk.18.ffn_norm.weight \| Block 18 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 174 \| blk.18.ffn_up.weight \| Block 18 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.18: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_19">Block 19 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 175 \| blk.19.attn_k.weight \| Block 19 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 176 \| blk.19.attn_norm.weight \| Block 19 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 177 \| blk.19.attn_output.weight \| Block 19 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 178 \| blk.19.attn_q.weight \| Block 19 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 179 \| blk.19.attn_v.weight \| Block 19 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 180 \| blk.19.ffn_down.weight \| Block 19 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 181 \| blk.19.ffn_gate.weight \| Block 19 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 182 \| blk.19.ffn_norm.weight \| Block 19 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 183 \| blk.19.ffn_up.weight \| Block 19 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.19: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_20">Block 20 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 184 \| blk.20.attn_k.weight \| Block 20 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 185 \| blk.20.attn_norm.weight \| Block 20 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 186 \| blk.20.attn_output.weight \| Block 20 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 187 \| blk.20.attn_q.weight \| Block 20 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 188 \| blk.20.attn_v.weight \| Block 20 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 189 \| blk.20.ffn_down.weight \| Block 20 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 190 \| blk.20.ffn_gate.weight \| Block 20 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 191 \| blk.20.ffn_norm.weight \| Block 20 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 192 \| blk.20.ffn_up.weight \| Block 20 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.20: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_21">Block 21 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 193 \| blk.21.attn_k.weight \| Block 21 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 194 \| blk.21.attn_norm.weight \| Block 21 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 195 \| blk.21.attn_output.weight \| Block 21 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 196 \| blk.21.attn_q.weight \| Block 21 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 197 \| blk.21.attn_v.weight \| Block 21 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 198 \| blk.21.ffn_down.weight \| Block 21 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 199 \| blk.21.ffn_gate.weight \| Block 21 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 200 \| blk.21.ffn_norm.weight \| Block 21 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 201 \| blk.21.ffn_up.weight \| Block 21 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.21: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_22">Block 22 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 202 \| blk.22.attn_k.weight \| Block 22 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 203 \| blk.22.attn_norm.weight \| Block 22 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 204 \| blk.22.attn_output.weight \| Block 22 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 205 \| blk.22.attn_q.weight \| Block 22 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 206 \| blk.22.attn_v.weight \| Block 22 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 207 \| blk.22.ffn_down.weight \| Block 22 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 208 \| blk.22.ffn_gate.weight \| Block 22 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 209 \| blk.22.ffn_norm.weight \| Block 22 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 210 \| blk.22.ffn_up.weight \| Block 22 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.22: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_23">Block 23 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 211 \| blk.23.attn_k.weight \| Block 23 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 212 \| blk.23.attn_norm.weight \| Block 23 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 213 \| blk.23.attn_output.weight \| Block 23 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 214 \| blk.23.attn_q.weight \| Block 23 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 215 \| blk.23.attn_v.weight \| Block 23 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 216 \| blk.23.ffn_down.weight \| Block 23 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 217 \| blk.23.ffn_gate.weight \| Block 23 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 218 \| blk.23.ffn_norm.weight \| Block 23 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 219 \| blk.23.ffn_up.weight \| Block 23 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.23: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_24">Block 24 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 220 \| blk.24.attn_k.weight \| Block 24 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 221 \| blk.24.attn_norm.weight \| Block 24 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 222 \| blk.24.attn_output.weight \| Block 24 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 223 \| blk.24.attn_q.weight \| Block 24 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 224 \| blk.24.attn_v.weight \| Block 24 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 225 \| blk.24.ffn_down.weight \| Block 24 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 226 \| blk.24.ffn_gate.weight \| Block 24 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 227 \| blk.24.ffn_norm.weight \| Block 24 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 228 \| blk.24.ffn_up.weight \| Block 24 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.24: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_25">Block 25 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 229 \| blk.25.attn_k.weight \| Block 25 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 230 \| blk.25.attn_norm.weight \| Block 25 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 231 \| blk.25.attn_output.weight \| Block 25 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 232 \| blk.25.attn_q.weight \| Block 25 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 233 \| blk.25.attn_v.weight \| Block 25 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 234 \| blk.25.ffn_down.weight \| Block 25 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 235 \| blk.25.ffn_gate.weight \| Block 25 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 236 \| blk.25.ffn_norm.weight \| Block 25 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 237 \| blk.25.ffn_up.weight \| Block 25 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.25: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_26">Block 26 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 238 \| blk.26.attn_k.weight \| Block 26 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 239 \| blk.26.attn_norm.weight \| Block 26 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 240 \| blk.26.attn_output.weight \| Block 26 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 241 \| blk.26.attn_q.weight \| Block 26 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 242 \| blk.26.attn_v.weight \| Block 26 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 243 \| blk.26.ffn_down.weight \| Block 26 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 244 \| blk.26.ffn_gate.weight \| Block 26 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 245 \| blk.26.ffn_norm.weight \| Block 26 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 246 \| blk.26.ffn_up.weight \| Block 26 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.26: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_27">Block 27 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 247 \| blk.27.attn_k.weight \| Block 27 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 248 \| blk.27.attn_norm.weight \| Block 27 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 249 \| blk.27.attn_output.weight \| Block 27 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 250 \| blk.27.attn_q.weight \| Block 27 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 251 \| blk.27.attn_v.weight \| Block 27 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 252 \| blk.27.ffn_down.weight \| Block 27 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 253 \| blk.27.ffn_gate.weight \| Block 27 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 254 \| blk.27.ffn_norm.weight \| Block 27 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 255 \| blk.27.ffn_up.weight \| Block 27 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.27: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_28">Block 28 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 256 \| blk.28.attn_k.weight \| Block 28 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 257 \| blk.28.attn_norm.weight \| Block 28 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 258 \| blk.28.attn_output.weight \| Block 28 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 259 \| blk.28.attn_q.weight \| Block 28 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 260 \| blk.28.attn_v.weight \| Block 28 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 261 \| blk.28.ffn_down.weight \| Block 28 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 262 \| blk.28.ffn_gate.weight \| Block 28 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 263 \| blk.28.ffn_norm.weight \| Block 28 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 264 \| blk.28.ffn_up.weight \| Block 28 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.28: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_29">Block 29 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 265 \| blk.29.attn_k.weight \| Block 29 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 266 \| blk.29.attn_norm.weight \| Block 29 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 267 \| blk.29.attn_output.weight \| Block 29 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 268 \| blk.29.attn_q.weight \| Block 29 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 269 \| blk.29.attn_v.weight \| Block 29 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 270 \| blk.29.ffn_down.weight \| Block 29 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 271 \| blk.29.ffn_gate.weight \| Block 29 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 272 \| blk.29.ffn_norm.weight \| Block 29 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 273 \| blk.29.ffn_up.weight \| Block 29 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.29: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_30">Block 30 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 274 \| blk.30.attn_k.weight \| Block 30 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 275 \| blk.30.attn_norm.weight \| Block 30 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 276 \| blk.30.attn_output.weight \| Block 30 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 277 \| blk.30.attn_q.weight \| Block 30 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 278 \| blk.30.attn_v.weight \| Block 30 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 279 \| blk.30.ffn_down.weight \| Block 30 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 280 \| blk.30.ffn_gate.weight \| Block 30 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 281 \| blk.30.ffn_norm.weight \| Block 30 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 282 \| blk.30.ffn_up.weight \| Block 30 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.30: (~218M) 218112000
	- Percentage of total elements: 2.72%


	### <a name="blk_31">Block 31 Tensor Group : ~218M Elements</a>

	\| T_ID \| Tensor Layer Name \| Human Friendly Tensor Layer Name \| Elements \| Shape \| Type \|
	\| ---: \| :------------------------ \| :---------------------------------------------- \| :-------------- \| :-------------------- \| :--- \|
	\| 283 \| blk.31.attn_k.weight \| Block 31 Attention Key (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q2_K \|
	\| 284 \| blk.31.attn_norm.weight \| Block 31 Attention Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 285 \| blk.31.attn_output.weight \| Block 31 Attention Output (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q5_K \|
	\| 286 \| blk.31.attn_q.weight \| Block 31 Attention Query (W) \| (~17M) 16777216 \| 4096 x 4096 x 1 x 1 \| Q2_K \|
	\| 287 \| blk.31.attn_v.weight \| Block 31 Attention Value (W) \| ( ~4M) 4194304 \| 4096 x 1024 x 1 x 1 \| Q4_K \|
	\| 288 \| blk.31.ffn_down.weight \| Block 31 Feed-Forward Network "Down" (W) \| (~59M) 58720256 \| 14336 x 4096 x 1 x 1 \| Q4_K \|
	\| 289 \| blk.31.ffn_gate.weight \| Block 31 Feed-Forward Network "Gate" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|
	\| 290 \| blk.31.ffn_norm.weight \| Block 31 Feed-Forward Network Normalization (W) \| ( ~4K) 4096 \| 4096 x 1 x 1 x 1 \| F32 \|
	\| 291 \| blk.31.ffn_up.weight \| Block 31 Feed-Forward Network "Up" (W) \| (~59M) 58720256 \| 4096 x 14336 x 1 x 1 \| Q2_K \|

	- Total elements in blk.31: (~218M) 218112000
	- Percentage of total elements: 2.72%