Instructions to use eaddario/Qwen3-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eaddario/Qwen3-30B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eaddario/Qwen3-30B-A3B-GGUF",
	filename="Qwen3-30B-A3B-F16-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eaddario/Qwen3-30B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf eaddario/Qwen3-30B-A3B-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/Qwen3-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eaddario/Qwen3-30B-A3B-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/Qwen3-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use eaddario/Qwen3-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eaddario/Qwen3-30B-A3B-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/Qwen3-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Ollama
How to use eaddario/Qwen3-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use eaddario/Qwen3-30B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eaddario/Qwen3-30B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for eaddario/Qwen3-30B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eaddario/Qwen3-30B-A3B-GGUF to start chatting

How to use eaddario/Qwen3-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use eaddario/Qwen3-30B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use eaddario/Qwen3-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use eaddario/Qwen3-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eaddario/Qwen3-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-30B-A3B-GGUF / scores /Qwen3-30B-A3B-Q3_K_S.md

eaddario

Add GGUF internal file structure

a630e18 verified about 1 year ago

preview code

raw

history blame

187 kB

Qwen3-30B-A3B-Q3_K_S.gguf - GGUF Internal File Dump

Endian: LITTLE endian

Key Value Metadata Store

There are 44 key-value pairs in this file

POS	TYPE	Count	Key	Value
1	UINT32	1	GGUF.version	3
2	UINT64	1	GGUF.tensor_count	579
3	UINT64	1	GGUF.kv_count	41
4	STRING	1	general.architecture	`qwen3moe`
5	STRING	1	general.type	`model`
6	STRING	1	general.name	`Qwen3 30B A3B`
7	STRING	1	general.basename	`Qwen3`
8	STRING	1	general.size_label	`30B-A3B`
9	STRING	1	general.license	`apache-2.0`
10	STRING	1	general.license.link	`https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE`
11	UINT32	1	general.base_model.count	1
12	STRING	1	general.base_model.0.name	`Qwen3 30B A3B Base`
13	STRING	1	general.base_model.0.organization	`Qwen`
14	STRING	1	general.base_model.0.repo_url	`https://huggingface.co/Qwen/Qwen3-30B-A3B-Base`
15	[STRING]	1	general.tags	[ `text-generation` ]
16	UINT32	1	qwen3moe.block_count	48
17	UINT32	1	qwen3moe.context_length	40960
18	UINT32	1	qwen3moe.embedding_length	2048
19	UINT32	1	qwen3moe.feed_forward_length	6144
20	UINT32	1	qwen3moe.attention.head_count	32
21	UINT32	1	qwen3moe.attention.head_count_kv	4
22	FLOAT32	1	qwen3moe.rope.freq_base	1000000.0
23	FLOAT32	1	qwen3moe.attention.layer_norm_rms_epsilon	1e-06
24	UINT32	1	qwen3moe.expert_used_count	8
25	UINT32	1	qwen3moe.attention.key_length	128
26	UINT32	1	qwen3moe.attention.value_length	128
27	UINT32	1	qwen3moe.expert_count	128
28	UINT32	1	qwen3moe.expert_feed_forward_length	768
29	STRING	1	tokenizer.ggml.model	`gpt2`
30	STRING	1	tokenizer.ggml.pre	`qwen2`
31	[STRING]	151936	tokenizer.ggml.tokens	[ `!`, `"`, `#`, `$`, `%`, ... ]
32	[INT32]	151936	tokenizer.ggml.token_type	[ 1, 1, 1, 1, 1, 1, 1, ... ]
33	[STRING]	151387	tokenizer.ggml.merges	[ `Ġ Ġ`, `ĠĠ ĠĠ`, `i n`, `Ġ t`, `ĠĠĠĠ ĠĠĠĠ`, ... ]
34	UINT32	1	tokenizer.ggml.eos_token_id	151645
35	UINT32	1	tokenizer.ggml.padding_token_id	151643
36	UINT32	1	tokenizer.ggml.bos_token_id	151643
37	BOOL	1	tokenizer.ggml.add_bos_token	False
38	STRING	1	tokenizer.chat_template	`{%- if tools %}{{- '<
39	UINT32	1	general.quantization_version	2
40	UINT32	1	general.file_type	11
41	STRING	1	quantize.imatrix.file	`./imatrix/imatrix-Qwen3-30B-A3B-large.dat`
42	STRING	1	quantize.imatrix.dataset	`../../datasets/imatrix/calibration_all_large.txt`
43	INT32	1	quantize.imatrix.entries_count	382
44	INT32	1	quantize.imatrix.chunks_count	4978

Tensors Overview ~31B Elements

Total number of elements in all tensors: 30532122624 Elements

Qwen3-30B-A3B-Q3_K_S.gguf - GGUF Internal File Dump
- Key Value Metadata Store
- Tensors Overview ~31B 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)
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1	output_norm.weight	0x85340c0	0x2000
2	token_embd.weight	0x85360c0	0x615f000
3	blk.0.attn_k.weight	0xe6950c0	0x54000
4	blk.0.attn_k_norm.weight	0xe6e90c0	0x200
5	blk.0.attn_norm.weight	0xe6e92c0	0x2000
6	blk.0.attn_output.weight	0xe6eb2c0	0x370000
7	blk.0.attn_q.weight	0xea5b2c0	0x2a0000
8	blk.0.attn_q_norm.weight	0xecfb2c0	0x200
9	blk.0.attn_v.weight	0xecfb4c0	0x6e000
10	blk.0.ffn_down_exps.weight	0xed694c0	0x5280000
11	blk.0.ffn_gate_exps.weight	0x13fe94c0	0x3f00000
12	blk.0.ffn_gate_inp.weight	0x17ee94c0	0x100000
13	blk.0.ffn_norm.weight	0x17fe94c0	0x2000
14	blk.0.ffn_up_exps.weight	0x17feb4c0	0x3f00000
15	blk.1.attn_k.weight	0x1beeb4c0	0x54000
16	blk.1.attn_k_norm.weight	0x1bf3f4c0	0x200
17	blk.1.attn_norm.weight	0x1bf3f6c0	0x2000
18	blk.1.attn_output.weight	0x1bf416c0	0x370000
19	blk.1.attn_q.weight	0x1c2b16c0	0x2a0000
20	blk.1.attn_q_norm.weight	0x1c5516c0	0x200
21	blk.1.attn_v.weight	0x1c5518c0	0x6e000
22	blk.1.ffn_down_exps.weight	0x1c5bf8c0	0x5280000
23	blk.1.ffn_gate_exps.weight	0x2183f8c0	0x3f00000
24	blk.1.ffn_gate_inp.weight	0x2573f8c0	0x100000
25	blk.1.ffn_norm.weight	0x2583f8c0	0x2000
26	blk.1.ffn_up_exps.weight	0x258418c0	0x3f00000
27	blk.2.attn_k.weight	0x297418c0	0x54000
28	blk.2.attn_k_norm.weight	0x297958c0	0x200
29	blk.2.attn_norm.weight	0x29795ac0	0x2000
30	blk.2.attn_output.weight	0x29797ac0	0x370000
31	blk.2.attn_q.weight	0x29b07ac0	0x2a0000
32	blk.2.attn_q_norm.weight	0x29da7ac0	0x200
33	blk.2.attn_v.weight	0x29da7cc0	0x6e000
34	blk.2.ffn_down_exps.weight	0x29e15cc0	0x6c00000
35	blk.2.ffn_gate_exps.weight	0x30a15cc0	0x3f00000
36	blk.2.ffn_gate_inp.weight	0x34915cc0	0x100000
37	blk.2.ffn_norm.weight	0x34a15cc0	0x2000
38	blk.2.ffn_up_exps.weight	0x34a17cc0	0x3f00000
39	blk.3.attn_k.weight	0x38917cc0	0x54000
40	blk.3.attn_k_norm.weight	0x3896bcc0	0x200
41	blk.3.attn_norm.weight	0x3896bec0	0x2000
42	blk.3.attn_output.weight	0x3896dec0	0x370000
43	blk.3.attn_q.weight	0x38cddec0	0x2a0000
44	blk.3.attn_q_norm.weight	0x38f7dec0	0x200
45	blk.3.attn_v.weight	0x38f7e0c0	0x6e000
46	blk.3.ffn_down_exps.weight	0x38fec0c0	0x5280000
47	blk.3.ffn_gate_exps.weight	0x3e26c0c0	0x3f00000
48	blk.3.ffn_gate_inp.weight	0x4216c0c0	0x100000
49	blk.3.ffn_norm.weight	0x4226c0c0	0x2000
50	blk.3.ffn_up_exps.weight	0x4226e0c0	0x3f00000
51	blk.4.attn_k.weight	0x4616e0c0	0x54000
52	blk.4.attn_k_norm.weight	0x461c20c0	0x200
53	blk.4.attn_norm.weight	0x461c22c0	0x2000
54	blk.4.attn_output.weight	0x461c42c0	0x370000
55	blk.4.attn_q.weight	0x465342c0	0x2a0000
56	blk.4.attn_q_norm.weight	0x467d42c0	0x200
57	blk.4.attn_v.weight	0x467d44c0	0x6e000
58	blk.4.ffn_down_exps.weight	0x468424c0	0x5280000
59	blk.4.ffn_gate_exps.weight	0x4bac24c0	0x3f00000
60	blk.4.ffn_gate_inp.weight	0x4f9c24c0	0x100000
61	blk.4.ffn_norm.weight	0x4fac24c0	0x2000
62	blk.4.ffn_up_exps.weight	0x4fac44c0	0x3f00000
63	blk.5.attn_k.weight	0x539c44c0	0x54000
64	blk.5.attn_k_norm.weight	0x53a184c0	0x200
65	blk.5.attn_norm.weight	0x53a186c0	0x2000
66	blk.5.attn_output.weight	0x53a1a6c0	0x370000
67	blk.5.attn_q.weight	0x53d8a6c0	0x2a0000
68	blk.5.attn_q_norm.weight	0x5402a6c0	0x200
69	blk.5.attn_v.weight	0x5402a8c0	0x6e000
70	blk.5.ffn_down_exps.weight	0x540988c0	0x5280000
71	blk.5.ffn_gate_exps.weight	0x593188c0	0x3f00000
72	blk.5.ffn_gate_inp.weight	0x5d2188c0	0x100000
73	blk.5.ffn_norm.weight	0x5d3188c0	0x2000
74	blk.5.ffn_up_exps.weight	0x5d31a8c0	0x3f00000
75	blk.6.attn_k.weight	0x6121a8c0	0x54000
76	blk.6.attn_k_norm.weight	0x6126e8c0	0x200
77	blk.6.attn_norm.weight	0x6126eac0	0x2000
78	blk.6.attn_output.weight	0x61270ac0	0x370000
79	blk.6.attn_q.weight	0x615e0ac0	0x2a0000
80	blk.6.attn_q_norm.weight	0x61880ac0	0x200
81	blk.6.attn_v.weight	0x61880cc0	0x6e000
82	blk.6.ffn_down_exps.weight	0x618eecc0	0x5280000
83	blk.6.ffn_gate_exps.weight	0x66b6ecc0	0x3f00000
84	blk.6.ffn_gate_inp.weight	0x6aa6ecc0	0x100000
85	blk.6.ffn_norm.weight	0x6ab6ecc0	0x2000
86	blk.6.ffn_up_exps.weight	0x6ab70cc0	0x3f00000
87	blk.7.attn_k.weight	0x6ea70cc0	0x54000
88	blk.7.attn_k_norm.weight	0x6eac4cc0	0x200
89	blk.7.attn_norm.weight	0x6eac4ec0	0x2000
90	blk.7.attn_output.weight	0x6eac6ec0	0x370000
91	blk.7.attn_q.weight	0x6ee36ec0	0x2a0000
92	blk.7.attn_q_norm.weight	0x6f0d6ec0	0x200
93	blk.7.attn_v.weight	0x6f0d70c0	0x6e000
94	blk.7.ffn_down_exps.weight	0x6f1450c0	0x5280000
95	blk.7.ffn_gate_exps.weight	0x743c50c0	0x3f00000
96	blk.7.ffn_gate_inp.weight	0x782c50c0	0x100000
97	blk.7.ffn_norm.weight	0x783c50c0	0x2000
98	blk.7.ffn_up_exps.weight	0x783c70c0	0x3f00000
99	blk.8.attn_k.weight	0x7c2c70c0	0x54000
100	blk.8.attn_k_norm.weight	0x7c31b0c0	0x200
101	blk.8.attn_norm.weight	0x7c31b2c0	0x2000
102	blk.8.attn_output.weight	0x7c31d2c0	0x370000
103	blk.8.attn_q.weight	0x7c68d2c0	0x2a0000
104	blk.8.attn_q_norm.weight	0x7c92d2c0	0x200
105	blk.8.attn_v.weight	0x7c92d4c0	0x6e000
106	blk.8.ffn_down_exps.weight	0x7c99b4c0	0x5280000
107	blk.8.ffn_gate_exps.weight	0x81c1b4c0	0x3f00000
108	blk.8.ffn_gate_inp.weight	0x85b1b4c0	0x100000
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130	blk.10.ffn_down_exps.weight	0x97a47cc0	0x5280000
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132	blk.10.ffn_gate_inp.weight	0xa0bc7cc0	0x100000
133	blk.10.ffn_norm.weight	0xa0cc7cc0	0x2000
134	blk.10.ffn_up_exps.weight	0xa0cc9cc0	0x3f00000
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136	blk.11.attn_k_norm.weight	0xa4c1dcc0	0x200
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138	blk.11.attn_output.weight	0xa4c1fec0	0x370000
139	blk.11.attn_q.weight	0xa4f8fec0	0x2a0000
140	blk.11.attn_q_norm.weight	0xa522fec0	0x200
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142	blk.11.ffn_down_exps.weight	0xa529e0c0	0x5280000
143	blk.11.ffn_gate_exps.weight	0xaa51e0c0	0x3f00000
144	blk.11.ffn_gate_inp.weight	0xae41e0c0	0x100000
145	blk.11.ffn_norm.weight	0xae51e0c0	0x2000
146	blk.11.ffn_up_exps.weight	0xae5200c0	0x3f00000
147	blk.12.attn_k.weight	0xb24200c0	0x54000
148	blk.12.attn_k_norm.weight	0xb24740c0	0x200
149	blk.12.attn_norm.weight	0xb24742c0	0x2000
150	blk.12.attn_output.weight	0xb24762c0	0x370000
151	blk.12.attn_q.weight	0xb27e62c0	0x2a0000
152	blk.12.attn_q_norm.weight	0xb2a862c0	0x200
153	blk.12.attn_v.weight	0xb2a864c0	0x6e000
154	blk.12.ffn_down_exps.weight	0xb2af44c0	0x5280000
155	blk.12.ffn_gate_exps.weight	0xb7d744c0	0x3f00000
156	blk.12.ffn_gate_inp.weight	0xbbc744c0	0x100000
157	blk.12.ffn_norm.weight	0xbbd744c0	0x2000
158	blk.12.ffn_up_exps.weight	0xbbd764c0	0x3f00000
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438	blk.36.attn_output.weight	0x22641e2c0	0x370000
439	blk.36.attn_q.weight	0x22678e2c0	0x370000
440	blk.36.attn_q_norm.weight	0x226afe2c0	0x200
441	blk.36.attn_v.weight	0x226afe4c0	0x6e000
442	blk.36.ffn_down_exps.weight	0x226b6c4c0	0x6c00000
443	blk.36.ffn_gate_exps.weight	0x22d76c4c0	0x5280000
444	blk.36.ffn_gate_inp.weight	0x2329ec4c0	0x100000
445	blk.36.ffn_norm.weight	0x232aec4c0	0x2000
446	blk.36.ffn_up_exps.weight	0x232aee4c0	0x5280000
447	blk.37.attn_k.weight	0x237d6e4c0	0x6e000
448	blk.37.attn_k_norm.weight	0x237ddc4c0	0x200
449	blk.37.attn_norm.weight	0x237ddc6c0	0x2000
450	blk.37.attn_output.weight	0x237dde6c0	0x370000
451	blk.37.attn_q.weight	0x23814e6c0	0x370000
452	blk.37.attn_q_norm.weight	0x2384be6c0	0x200
453	blk.37.attn_v.weight	0x2384be8c0	0x6e000
454	blk.37.ffn_down_exps.weight	0x23852c8c0	0x6c00000
455	blk.37.ffn_gate_exps.weight	0x23f12c8c0	0x5280000
456	blk.37.ffn_gate_inp.weight	0x2443ac8c0	0x100000
457	blk.37.ffn_norm.weight	0x2444ac8c0	0x2000
458	blk.37.ffn_up_exps.weight	0x2444ae8c0	0x5280000
459	blk.38.attn_k.weight	0x24972e8c0	0x6e000
460	blk.38.attn_k_norm.weight	0x24979c8c0	0x200
461	blk.38.attn_norm.weight	0x24979cac0	0x2000
462	blk.38.attn_output.weight	0x24979eac0	0x370000
463	blk.38.attn_q.weight	0x249b0eac0	0x370000
464	blk.38.attn_q_norm.weight	0x249e7eac0	0x200
465	blk.38.attn_v.weight	0x249e7ecc0	0x6e000
466	blk.38.ffn_down_exps.weight	0x249eeccc0	0x6c00000
467	blk.38.ffn_gate_exps.weight	0x250aeccc0	0x5280000
468	blk.38.ffn_gate_inp.weight	0x255d6ccc0	0x100000
469	blk.38.ffn_norm.weight	0x255e6ccc0	0x2000
470	blk.38.ffn_up_exps.weight	0x255e6ecc0	0x5280000
471	blk.39.attn_k.weight	0x25b0eecc0	0x6e000
472	blk.39.attn_k_norm.weight	0x25b15ccc0	0x200
473	blk.39.attn_norm.weight	0x25b15cec0	0x2000
474	blk.39.attn_output.weight	0x25b15eec0	0x370000
475	blk.39.attn_q.weight	0x25b4ceec0	0x370000
476	blk.39.attn_q_norm.weight	0x25b83eec0	0x200
477	blk.39.attn_v.weight	0x25b83f0c0	0x6e000
478	blk.39.ffn_down_exps.weight	0x25b8ad0c0	0x6c00000
479	blk.39.ffn_gate_exps.weight	0x2624ad0c0	0x5280000
480	blk.39.ffn_gate_inp.weight	0x26772d0c0	0x100000
481	blk.39.ffn_norm.weight	0x26782d0c0	0x2000
482	blk.39.ffn_up_exps.weight	0x26782f0c0	0x5280000
483	blk.40.attn_k.weight	0x26caaf0c0	0x6e000
484	blk.40.attn_k_norm.weight	0x26cb1d0c0	0x200
485	blk.40.attn_norm.weight	0x26cb1d2c0	0x2000
486	blk.40.attn_output.weight	0x26cb1f2c0	0x370000
487	blk.40.attn_q.weight	0x26ce8f2c0	0x370000
488	blk.40.attn_q_norm.weight	0x26d1ff2c0	0x200
489	blk.40.attn_v.weight	0x26d1ff4c0	0x6e000
490	blk.40.ffn_down_exps.weight	0x26d26d4c0	0x6c00000
491	blk.40.ffn_gate_exps.weight	0x273e6d4c0	0x5280000
492	blk.40.ffn_gate_inp.weight	0x2790ed4c0	0x100000
493	blk.40.ffn_norm.weight	0x2791ed4c0	0x2000
494	blk.40.ffn_up_exps.weight	0x2791ef4c0	0x5280000
495	blk.41.attn_k.weight	0x27e46f4c0	0x6e000
496	blk.41.attn_k_norm.weight	0x27e4dd4c0	0x200
497	blk.41.attn_norm.weight	0x27e4dd6c0	0x2000
498	blk.41.attn_output.weight	0x27e4df6c0	0x370000
499	blk.41.attn_q.weight	0x27e84f6c0	0x370000
500	blk.41.attn_q_norm.weight	0x27ebbf6c0	0x200
501	blk.41.attn_v.weight	0x27ebbf8c0	0x6e000
502	blk.41.ffn_down_exps.weight	0x27ec2d8c0	0x6c00000
503	blk.41.ffn_gate_exps.weight	0x28582d8c0	0x5280000
504	blk.41.ffn_gate_inp.weight	0x28aaad8c0	0x100000
505	blk.41.ffn_norm.weight	0x28abad8c0	0x2000
506	blk.41.ffn_up_exps.weight	0x28abaf8c0	0x5280000
507	blk.42.attn_k.weight	0x28fe2f8c0	0x6e000
508	blk.42.attn_k_norm.weight	0x28fe9d8c0	0x200
509	blk.42.attn_norm.weight	0x28fe9dac0	0x2000
510	blk.42.attn_output.weight	0x28fe9fac0	0x370000
511	blk.42.attn_q.weight	0x29020fac0	0x370000
512	blk.42.attn_q_norm.weight	0x29057fac0	0x200
513	blk.42.attn_v.weight	0x29057fcc0	0x6e000
514	blk.42.ffn_down_exps.weight	0x2905edcc0	0x6c00000
515	blk.42.ffn_gate_exps.weight	0x2971edcc0	0x5280000
516	blk.42.ffn_gate_inp.weight	0x29c46dcc0	0x100000
517	blk.42.ffn_norm.weight	0x29c56dcc0	0x2000
518	blk.42.ffn_up_exps.weight	0x29c56fcc0	0x5280000
519	blk.43.attn_k.weight	0x2a17efcc0	0x6e000
520	blk.43.attn_k_norm.weight	0x2a185dcc0	0x200
521	blk.43.attn_norm.weight	0x2a185dec0	0x2000
522	blk.43.attn_output.weight	0x2a185fec0	0x370000
523	blk.43.attn_q.weight	0x2a1bcfec0	0x370000
524	blk.43.attn_q_norm.weight	0x2a1f3fec0	0x200
525	blk.43.attn_v.weight	0x2a1f400c0	0x6e000
526	blk.43.ffn_down_exps.weight	0x2a1fae0c0	0x6c00000
527	blk.43.ffn_gate_exps.weight	0x2a8bae0c0	0x5280000
528	blk.43.ffn_gate_inp.weight	0x2ade2e0c0	0x100000
529	blk.43.ffn_norm.weight	0x2adf2e0c0	0x2000
530	blk.43.ffn_up_exps.weight	0x2adf300c0	0x5280000
531	blk.44.attn_k.weight	0x2b31b00c0	0x6e000
532	blk.44.attn_k_norm.weight	0x2b321e0c0	0x200
533	blk.44.attn_norm.weight	0x2b321e2c0	0x2000
534	blk.44.attn_output.weight	0x2b32202c0	0x370000
535	blk.44.attn_q.weight	0x2b35902c0	0x370000
536	blk.44.attn_q_norm.weight	0x2b39002c0	0x200
537	blk.44.attn_v.weight	0x2b39004c0	0x6e000
538	blk.44.ffn_down_exps.weight	0x2b396e4c0	0x6c00000
539	blk.44.ffn_gate_exps.weight	0x2ba56e4c0	0x5280000
540	blk.44.ffn_gate_inp.weight	0x2bf7ee4c0	0x100000
541	blk.44.ffn_norm.weight	0x2bf8ee4c0	0x2000
542	blk.44.ffn_up_exps.weight	0x2bf8f04c0	0x5280000
543	blk.45.attn_k.weight	0x2c4b704c0	0x6e000
544	blk.45.attn_k_norm.weight	0x2c4bde4c0	0x200
545	blk.45.attn_norm.weight	0x2c4bde6c0	0x2000
546	blk.45.attn_output.weight	0x2c4be06c0	0x370000
547	blk.45.attn_q.weight	0x2c4f506c0	0x370000
548	blk.45.attn_q_norm.weight	0x2c52c06c0	0x200
549	blk.45.attn_v.weight	0x2c52c08c0	0x6e000
550	blk.45.ffn_down_exps.weight	0x2c532e8c0	0x6c00000
551	blk.45.ffn_gate_exps.weight	0x2cbf2e8c0	0x5280000
552	blk.45.ffn_gate_inp.weight	0x2d11ae8c0	0x100000
553	blk.45.ffn_norm.weight	0x2d12ae8c0	0x2000
554	blk.45.ffn_up_exps.weight	0x2d12b08c0	0x5280000
555	blk.46.attn_k.weight	0x2d65308c0	0x6e000
556	blk.46.attn_k_norm.weight	0x2d659e8c0	0x200
557	blk.46.attn_norm.weight	0x2d659eac0	0x2000
558	blk.46.attn_output.weight	0x2d65a0ac0	0x370000
559	blk.46.attn_q.weight	0x2d6910ac0	0x370000
560	blk.46.attn_q_norm.weight	0x2d6c80ac0	0x200
561	blk.46.attn_v.weight	0x2d6c80cc0	0x6e000
562	blk.46.ffn_down_exps.weight	0x2d6ceecc0	0x6c00000
563	blk.46.ffn_gate_exps.weight	0x2dd8eecc0	0x5280000
564	blk.46.ffn_gate_inp.weight	0x2e2b6ecc0	0x100000
565	blk.46.ffn_norm.weight	0x2e2c6ecc0	0x2000
566	blk.46.ffn_up_exps.weight	0x2e2c70cc0	0x5280000
567	blk.47.attn_k.weight	0x2e7ef0cc0	0x6e000
568	blk.47.attn_k_norm.weight	0x2e7f5ecc0	0x200
569	blk.47.attn_norm.weight	0x2e7f5eec0	0x2000
570	blk.47.attn_output.weight	0x2e7f60ec0	0x370000
571	blk.47.attn_q.weight	0x2e82d0ec0	0x370000
572	blk.47.attn_q_norm.weight	0x2e8640ec0	0x200
573	blk.47.attn_v.weight	0x2e86410c0	0x6e000
574	blk.47.ffn_down_exps.weight	0x2e86af0c0	0x6c00000
575	blk.47.ffn_gate_exps.weight	0x2ef2af0c0	0x5280000
576	blk.47.ffn_gate_inp.weight	0x2f452f0c0	0x100000
577	blk.47.ffn_norm.weight	0x2f462f0c0	0x2000
578	blk.47.ffn_up_exps.weight	0x2f46310c0	0x5280000

Base Tensor Group : ~622M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
0	output.weight	Output (W)	(~311M) 311164928	2048 x 151936 x 1 x 1	Q3_K
1	output_norm.weight	Output Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
2	token_embd.weight	Token Embedding (W)	(~311M) 311164928	2048 x 151936 x 1 x 1	Q2_K

Total elements in base: (~622M) 622331904
Percentage of total elements: 2.04%

Block 0 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
3	blk.0.attn_k.weight	Block 0 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
4	blk.0.attn_k_norm.weight	Block 0 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
5	blk.0.attn_norm.weight	Block 0 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
6	blk.0.attn_output.weight	Block 0 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
7	blk.0.attn_q.weight	Block 0 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
8	blk.0.attn_q_norm.weight	Block 0 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
9	blk.0.attn_v.weight	Block 0 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
10	blk.0.ffn_down_exps.weight	Block 0 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
11	blk.0.ffn_gate_exps.weight	Block 0 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
12	blk.0.ffn_gate_inp.weight	Block 0 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
13	blk.0.ffn_norm.weight	Block 0 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
14	blk.0.ffn_up_exps.weight	Block 0 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.0: (~623M) 623120640
Percentage of total elements: 2.04%

Block 1 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
15	blk.1.attn_k.weight	Block 1 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
16	blk.1.attn_k_norm.weight	Block 1 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
17	blk.1.attn_norm.weight	Block 1 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
18	blk.1.attn_output.weight	Block 1 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
19	blk.1.attn_q.weight	Block 1 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
20	blk.1.attn_q_norm.weight	Block 1 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
21	blk.1.attn_v.weight	Block 1 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
22	blk.1.ffn_down_exps.weight	Block 1 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
23	blk.1.ffn_gate_exps.weight	Block 1 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
24	blk.1.ffn_gate_inp.weight	Block 1 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
25	blk.1.ffn_norm.weight	Block 1 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
26	blk.1.ffn_up_exps.weight	Block 1 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.1: (~623M) 623120640
Percentage of total elements: 2.04%

Block 2 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
27	blk.2.attn_k.weight	Block 2 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
28	blk.2.attn_k_norm.weight	Block 2 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
29	blk.2.attn_norm.weight	Block 2 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
30	blk.2.attn_output.weight	Block 2 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
31	blk.2.attn_q.weight	Block 2 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
32	blk.2.attn_q_norm.weight	Block 2 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
33	blk.2.attn_v.weight	Block 2 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
34	blk.2.ffn_down_exps.weight	Block 2 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
35	blk.2.ffn_gate_exps.weight	Block 2 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
36	blk.2.ffn_gate_inp.weight	Block 2 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
37	blk.2.ffn_norm.weight	Block 2 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
38	blk.2.ffn_up_exps.weight	Block 2 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.2: (~623M) 623120640
Percentage of total elements: 2.04%

Block 3 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
39	blk.3.attn_k.weight	Block 3 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
40	blk.3.attn_k_norm.weight	Block 3 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
41	blk.3.attn_norm.weight	Block 3 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
42	blk.3.attn_output.weight	Block 3 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
43	blk.3.attn_q.weight	Block 3 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
44	blk.3.attn_q_norm.weight	Block 3 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
45	blk.3.attn_v.weight	Block 3 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
46	blk.3.ffn_down_exps.weight	Block 3 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
47	blk.3.ffn_gate_exps.weight	Block 3 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
48	blk.3.ffn_gate_inp.weight	Block 3 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
49	blk.3.ffn_norm.weight	Block 3 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
50	blk.3.ffn_up_exps.weight	Block 3 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.3: (~623M) 623120640
Percentage of total elements: 2.04%

Block 4 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
51	blk.4.attn_k.weight	Block 4 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
52	blk.4.attn_k_norm.weight	Block 4 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
53	blk.4.attn_norm.weight	Block 4 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
54	blk.4.attn_output.weight	Block 4 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
55	blk.4.attn_q.weight	Block 4 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
56	blk.4.attn_q_norm.weight	Block 4 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
57	blk.4.attn_v.weight	Block 4 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
58	blk.4.ffn_down_exps.weight	Block 4 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
59	blk.4.ffn_gate_exps.weight	Block 4 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
60	blk.4.ffn_gate_inp.weight	Block 4 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
61	blk.4.ffn_norm.weight	Block 4 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
62	blk.4.ffn_up_exps.weight	Block 4 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.4: (~623M) 623120640
Percentage of total elements: 2.04%

Block 5 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
63	blk.5.attn_k.weight	Block 5 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
64	blk.5.attn_k_norm.weight	Block 5 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
65	blk.5.attn_norm.weight	Block 5 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
66	blk.5.attn_output.weight	Block 5 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
67	blk.5.attn_q.weight	Block 5 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
68	blk.5.attn_q_norm.weight	Block 5 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
69	blk.5.attn_v.weight	Block 5 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
70	blk.5.ffn_down_exps.weight	Block 5 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
71	blk.5.ffn_gate_exps.weight	Block 5 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
72	blk.5.ffn_gate_inp.weight	Block 5 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
73	blk.5.ffn_norm.weight	Block 5 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
74	blk.5.ffn_up_exps.weight	Block 5 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.5: (~623M) 623120640
Percentage of total elements: 2.04%

Block 6 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
75	blk.6.attn_k.weight	Block 6 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
76	blk.6.attn_k_norm.weight	Block 6 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
77	blk.6.attn_norm.weight	Block 6 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
78	blk.6.attn_output.weight	Block 6 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
79	blk.6.attn_q.weight	Block 6 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
80	blk.6.attn_q_norm.weight	Block 6 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
81	blk.6.attn_v.weight	Block 6 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
82	blk.6.ffn_down_exps.weight	Block 6 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
83	blk.6.ffn_gate_exps.weight	Block 6 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
84	blk.6.ffn_gate_inp.weight	Block 6 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
85	blk.6.ffn_norm.weight	Block 6 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
86	blk.6.ffn_up_exps.weight	Block 6 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.6: (~623M) 623120640
Percentage of total elements: 2.04%

Block 7 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
87	blk.7.attn_k.weight	Block 7 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
88	blk.7.attn_k_norm.weight	Block 7 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
89	blk.7.attn_norm.weight	Block 7 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
90	blk.7.attn_output.weight	Block 7 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
91	blk.7.attn_q.weight	Block 7 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
92	blk.7.attn_q_norm.weight	Block 7 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
93	blk.7.attn_v.weight	Block 7 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
94	blk.7.ffn_down_exps.weight	Block 7 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
95	blk.7.ffn_gate_exps.weight	Block 7 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
96	blk.7.ffn_gate_inp.weight	Block 7 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
97	blk.7.ffn_norm.weight	Block 7 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
98	blk.7.ffn_up_exps.weight	Block 7 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.7: (~623M) 623120640
Percentage of total elements: 2.04%

Block 8 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
99	blk.8.attn_k.weight	Block 8 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
100	blk.8.attn_k_norm.weight	Block 8 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
101	blk.8.attn_norm.weight	Block 8 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
102	blk.8.attn_output.weight	Block 8 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
103	blk.8.attn_q.weight	Block 8 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
104	blk.8.attn_q_norm.weight	Block 8 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
105	blk.8.attn_v.weight	Block 8 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
106	blk.8.ffn_down_exps.weight	Block 8 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
107	blk.8.ffn_gate_exps.weight	Block 8 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
108	blk.8.ffn_gate_inp.weight	Block 8 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
109	blk.8.ffn_norm.weight	Block 8 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
110	blk.8.ffn_up_exps.weight	Block 8 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.8: (~623M) 623120640
Percentage of total elements: 2.04%

Block 9 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
111	blk.9.attn_k.weight	Block 9 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
112	blk.9.attn_k_norm.weight	Block 9 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
113	blk.9.attn_norm.weight	Block 9 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
114	blk.9.attn_output.weight	Block 9 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
115	blk.9.attn_q.weight	Block 9 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
116	blk.9.attn_q_norm.weight	Block 9 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
117	blk.9.attn_v.weight	Block 9 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
118	blk.9.ffn_down_exps.weight	Block 9 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
119	blk.9.ffn_gate_exps.weight	Block 9 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
120	blk.9.ffn_gate_inp.weight	Block 9 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
121	blk.9.ffn_norm.weight	Block 9 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
122	blk.9.ffn_up_exps.weight	Block 9 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.9: (~623M) 623120640
Percentage of total elements: 2.04%

Block 10 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
123	blk.10.attn_k.weight	Block 10 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
124	blk.10.attn_k_norm.weight	Block 10 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
125	blk.10.attn_norm.weight	Block 10 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
126	blk.10.attn_output.weight	Block 10 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
127	blk.10.attn_q.weight	Block 10 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
128	blk.10.attn_q_norm.weight	Block 10 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
129	blk.10.attn_v.weight	Block 10 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
130	blk.10.ffn_down_exps.weight	Block 10 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
131	blk.10.ffn_gate_exps.weight	Block 10 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
132	blk.10.ffn_gate_inp.weight	Block 10 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
133	blk.10.ffn_norm.weight	Block 10 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
134	blk.10.ffn_up_exps.weight	Block 10 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.10: (~623M) 623120640
Percentage of total elements: 2.04%

Block 11 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
135	blk.11.attn_k.weight	Block 11 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
136	blk.11.attn_k_norm.weight	Block 11 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
137	blk.11.attn_norm.weight	Block 11 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
138	blk.11.attn_output.weight	Block 11 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
139	blk.11.attn_q.weight	Block 11 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
140	blk.11.attn_q_norm.weight	Block 11 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
141	blk.11.attn_v.weight	Block 11 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
142	blk.11.ffn_down_exps.weight	Block 11 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
143	blk.11.ffn_gate_exps.weight	Block 11 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
144	blk.11.ffn_gate_inp.weight	Block 11 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
145	blk.11.ffn_norm.weight	Block 11 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
146	blk.11.ffn_up_exps.weight	Block 11 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.11: (~623M) 623120640
Percentage of total elements: 2.04%

Block 12 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
147	blk.12.attn_k.weight	Block 12 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
148	blk.12.attn_k_norm.weight	Block 12 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
149	blk.12.attn_norm.weight	Block 12 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
150	blk.12.attn_output.weight	Block 12 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
151	blk.12.attn_q.weight	Block 12 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
152	blk.12.attn_q_norm.weight	Block 12 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
153	blk.12.attn_v.weight	Block 12 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
154	blk.12.ffn_down_exps.weight	Block 12 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
155	blk.12.ffn_gate_exps.weight	Block 12 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
156	blk.12.ffn_gate_inp.weight	Block 12 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
157	blk.12.ffn_norm.weight	Block 12 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
158	blk.12.ffn_up_exps.weight	Block 12 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.12: (~623M) 623120640
Percentage of total elements: 2.04%

Block 13 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
159	blk.13.attn_k.weight	Block 13 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
160	blk.13.attn_k_norm.weight	Block 13 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
161	blk.13.attn_norm.weight	Block 13 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
162	blk.13.attn_output.weight	Block 13 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
163	blk.13.attn_q.weight	Block 13 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
164	blk.13.attn_q_norm.weight	Block 13 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
165	blk.13.attn_v.weight	Block 13 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
166	blk.13.ffn_down_exps.weight	Block 13 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
167	blk.13.ffn_gate_exps.weight	Block 13 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
168	blk.13.ffn_gate_inp.weight	Block 13 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
169	blk.13.ffn_norm.weight	Block 13 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
170	blk.13.ffn_up_exps.weight	Block 13 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.13: (~623M) 623120640
Percentage of total elements: 2.04%

Block 14 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
171	blk.14.attn_k.weight	Block 14 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
172	blk.14.attn_k_norm.weight	Block 14 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
173	blk.14.attn_norm.weight	Block 14 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
174	blk.14.attn_output.weight	Block 14 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
175	blk.14.attn_q.weight	Block 14 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
176	blk.14.attn_q_norm.weight	Block 14 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
177	blk.14.attn_v.weight	Block 14 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
178	blk.14.ffn_down_exps.weight	Block 14 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
179	blk.14.ffn_gate_exps.weight	Block 14 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
180	blk.14.ffn_gate_inp.weight	Block 14 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
181	blk.14.ffn_norm.weight	Block 14 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
182	blk.14.ffn_up_exps.weight	Block 14 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.14: (~623M) 623120640
Percentage of total elements: 2.04%

Block 15 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
183	blk.15.attn_k.weight	Block 15 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
184	blk.15.attn_k_norm.weight	Block 15 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
185	blk.15.attn_norm.weight	Block 15 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
186	blk.15.attn_output.weight	Block 15 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
187	blk.15.attn_q.weight	Block 15 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
188	blk.15.attn_q_norm.weight	Block 15 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
189	blk.15.attn_v.weight	Block 15 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
190	blk.15.ffn_down_exps.weight	Block 15 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
191	blk.15.ffn_gate_exps.weight	Block 15 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
192	blk.15.ffn_gate_inp.weight	Block 15 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
193	blk.15.ffn_norm.weight	Block 15 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
194	blk.15.ffn_up_exps.weight	Block 15 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.15: (~623M) 623120640
Percentage of total elements: 2.04%

Block 16 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
195	blk.16.attn_k.weight	Block 16 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
196	blk.16.attn_k_norm.weight	Block 16 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
197	blk.16.attn_norm.weight	Block 16 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
198	blk.16.attn_output.weight	Block 16 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
199	blk.16.attn_q.weight	Block 16 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
200	blk.16.attn_q_norm.weight	Block 16 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
201	blk.16.attn_v.weight	Block 16 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
202	blk.16.ffn_down_exps.weight	Block 16 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
203	blk.16.ffn_gate_exps.weight	Block 16 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
204	blk.16.ffn_gate_inp.weight	Block 16 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
205	blk.16.ffn_norm.weight	Block 16 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
206	blk.16.ffn_up_exps.weight	Block 16 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.16: (~623M) 623120640
Percentage of total elements: 2.04%

Block 17 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
207	blk.17.attn_k.weight	Block 17 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
208	blk.17.attn_k_norm.weight	Block 17 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
209	blk.17.attn_norm.weight	Block 17 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
210	blk.17.attn_output.weight	Block 17 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
211	blk.17.attn_q.weight	Block 17 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
212	blk.17.attn_q_norm.weight	Block 17 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
213	blk.17.attn_v.weight	Block 17 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
214	blk.17.ffn_down_exps.weight	Block 17 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
215	blk.17.ffn_gate_exps.weight	Block 17 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
216	blk.17.ffn_gate_inp.weight	Block 17 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
217	blk.17.ffn_norm.weight	Block 17 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
218	blk.17.ffn_up_exps.weight	Block 17 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.17: (~623M) 623120640
Percentage of total elements: 2.04%

Block 18 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
219	blk.18.attn_k.weight	Block 18 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
220	blk.18.attn_k_norm.weight	Block 18 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
221	blk.18.attn_norm.weight	Block 18 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
222	blk.18.attn_output.weight	Block 18 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
223	blk.18.attn_q.weight	Block 18 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
224	blk.18.attn_q_norm.weight	Block 18 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
225	blk.18.attn_v.weight	Block 18 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
226	blk.18.ffn_down_exps.weight	Block 18 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
227	blk.18.ffn_gate_exps.weight	Block 18 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
228	blk.18.ffn_gate_inp.weight	Block 18 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
229	blk.18.ffn_norm.weight	Block 18 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
230	blk.18.ffn_up_exps.weight	Block 18 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.18: (~623M) 623120640
Percentage of total elements: 2.04%

Block 19 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
231	blk.19.attn_k.weight	Block 19 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
232	blk.19.attn_k_norm.weight	Block 19 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
233	blk.19.attn_norm.weight	Block 19 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
234	blk.19.attn_output.weight	Block 19 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
235	blk.19.attn_q.weight	Block 19 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
236	blk.19.attn_q_norm.weight	Block 19 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
237	blk.19.attn_v.weight	Block 19 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
238	blk.19.ffn_down_exps.weight	Block 19 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
239	blk.19.ffn_gate_exps.weight	Block 19 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
240	blk.19.ffn_gate_inp.weight	Block 19 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
241	blk.19.ffn_norm.weight	Block 19 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
242	blk.19.ffn_up_exps.weight	Block 19 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.19: (~623M) 623120640
Percentage of total elements: 2.04%

Block 20 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
243	blk.20.attn_k.weight	Block 20 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
244	blk.20.attn_k_norm.weight	Block 20 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
245	blk.20.attn_norm.weight	Block 20 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
246	blk.20.attn_output.weight	Block 20 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
247	blk.20.attn_q.weight	Block 20 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
248	blk.20.attn_q_norm.weight	Block 20 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
249	blk.20.attn_v.weight	Block 20 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
250	blk.20.ffn_down_exps.weight	Block 20 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
251	blk.20.ffn_gate_exps.weight	Block 20 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
252	blk.20.ffn_gate_inp.weight	Block 20 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
253	blk.20.ffn_norm.weight	Block 20 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
254	blk.20.ffn_up_exps.weight	Block 20 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.20: (~623M) 623120640
Percentage of total elements: 2.04%

Block 21 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
255	blk.21.attn_k.weight	Block 21 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
256	blk.21.attn_k_norm.weight	Block 21 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
257	blk.21.attn_norm.weight	Block 21 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
258	blk.21.attn_output.weight	Block 21 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
259	blk.21.attn_q.weight	Block 21 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
260	blk.21.attn_q_norm.weight	Block 21 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
261	blk.21.attn_v.weight	Block 21 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
262	blk.21.ffn_down_exps.weight	Block 21 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
263	blk.21.ffn_gate_exps.weight	Block 21 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
264	blk.21.ffn_gate_inp.weight	Block 21 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
265	blk.21.ffn_norm.weight	Block 21 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
266	blk.21.ffn_up_exps.weight	Block 21 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.21: (~623M) 623120640
Percentage of total elements: 2.04%

Block 22 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
267	blk.22.attn_k.weight	Block 22 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
268	blk.22.attn_k_norm.weight	Block 22 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
269	blk.22.attn_norm.weight	Block 22 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
270	blk.22.attn_output.weight	Block 22 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
271	blk.22.attn_q.weight	Block 22 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
272	blk.22.attn_q_norm.weight	Block 22 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
273	blk.22.attn_v.weight	Block 22 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
274	blk.22.ffn_down_exps.weight	Block 22 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
275	blk.22.ffn_gate_exps.weight	Block 22 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
276	blk.22.ffn_gate_inp.weight	Block 22 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
277	blk.22.ffn_norm.weight	Block 22 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
278	blk.22.ffn_up_exps.weight	Block 22 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.22: (~623M) 623120640
Percentage of total elements: 2.04%

Block 23 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
279	blk.23.attn_k.weight	Block 23 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q2_K
280	blk.23.attn_k_norm.weight	Block 23 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
281	blk.23.attn_norm.weight	Block 23 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
282	blk.23.attn_output.weight	Block 23 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
283	blk.23.attn_q.weight	Block 23 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q2_K
284	blk.23.attn_q_norm.weight	Block 23 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
285	blk.23.attn_v.weight	Block 23 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
286	blk.23.ffn_down_exps.weight	Block 23 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
287	blk.23.ffn_gate_exps.weight	Block 23 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
288	blk.23.ffn_gate_inp.weight	Block 23 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
289	blk.23.ffn_norm.weight	Block 23 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
290	blk.23.ffn_up_exps.weight	Block 23 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.23: (~623M) 623120640
Percentage of total elements: 2.04%

Block 24 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
291	blk.24.attn_k.weight	Block 24 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
292	blk.24.attn_k_norm.weight	Block 24 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
293	blk.24.attn_norm.weight	Block 24 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
294	blk.24.attn_output.weight	Block 24 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
295	blk.24.attn_q.weight	Block 24 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
296	blk.24.attn_q_norm.weight	Block 24 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
297	blk.24.attn_v.weight	Block 24 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
298	blk.24.ffn_down_exps.weight	Block 24 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
299	blk.24.ffn_gate_exps.weight	Block 24 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
300	blk.24.ffn_gate_inp.weight	Block 24 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
301	blk.24.ffn_norm.weight	Block 24 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
302	blk.24.ffn_up_exps.weight	Block 24 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.24: (~623M) 623120640
Percentage of total elements: 2.04%

Block 25 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
303	blk.25.attn_k.weight	Block 25 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
304	blk.25.attn_k_norm.weight	Block 25 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
305	blk.25.attn_norm.weight	Block 25 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
306	blk.25.attn_output.weight	Block 25 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
307	blk.25.attn_q.weight	Block 25 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
308	blk.25.attn_q_norm.weight	Block 25 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
309	blk.25.attn_v.weight	Block 25 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
310	blk.25.ffn_down_exps.weight	Block 25 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
311	blk.25.ffn_gate_exps.weight	Block 25 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
312	blk.25.ffn_gate_inp.weight	Block 25 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
313	blk.25.ffn_norm.weight	Block 25 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
314	blk.25.ffn_up_exps.weight	Block 25 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.25: (~623M) 623120640
Percentage of total elements: 2.04%

Block 26 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
315	blk.26.attn_k.weight	Block 26 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
316	blk.26.attn_k_norm.weight	Block 26 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
317	blk.26.attn_norm.weight	Block 26 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
318	blk.26.attn_output.weight	Block 26 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
319	blk.26.attn_q.weight	Block 26 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
320	blk.26.attn_q_norm.weight	Block 26 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
321	blk.26.attn_v.weight	Block 26 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
322	blk.26.ffn_down_exps.weight	Block 26 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
323	blk.26.ffn_gate_exps.weight	Block 26 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K
324	blk.26.ffn_gate_inp.weight	Block 26 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
325	blk.26.ffn_norm.weight	Block 26 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
326	blk.26.ffn_up_exps.weight	Block 26 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q2_K

Total elements in blk.26: (~623M) 623120640
Percentage of total elements: 2.04%

Block 27 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
327	blk.27.attn_k.weight	Block 27 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
328	blk.27.attn_k_norm.weight	Block 27 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
329	blk.27.attn_norm.weight	Block 27 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
330	blk.27.attn_output.weight	Block 27 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
331	blk.27.attn_q.weight	Block 27 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
332	blk.27.attn_q_norm.weight	Block 27 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
333	blk.27.attn_v.weight	Block 27 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
334	blk.27.ffn_down_exps.weight	Block 27 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q3_K
335	blk.27.ffn_gate_exps.weight	Block 27 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
336	blk.27.ffn_gate_inp.weight	Block 27 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
337	blk.27.ffn_norm.weight	Block 27 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
338	blk.27.ffn_up_exps.weight	Block 27 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.27: (~623M) 623120640
Percentage of total elements: 2.04%

Block 28 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
339	blk.28.attn_k.weight	Block 28 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
340	blk.28.attn_k_norm.weight	Block 28 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
341	blk.28.attn_norm.weight	Block 28 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
342	blk.28.attn_output.weight	Block 28 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
343	blk.28.attn_q.weight	Block 28 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
344	blk.28.attn_q_norm.weight	Block 28 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
345	blk.28.attn_v.weight	Block 28 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
346	blk.28.ffn_down_exps.weight	Block 28 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
347	blk.28.ffn_gate_exps.weight	Block 28 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
348	blk.28.ffn_gate_inp.weight	Block 28 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
349	blk.28.ffn_norm.weight	Block 28 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
350	blk.28.ffn_up_exps.weight	Block 28 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.28: (~623M) 623120640
Percentage of total elements: 2.04%

Block 29 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
351	blk.29.attn_k.weight	Block 29 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
352	blk.29.attn_k_norm.weight	Block 29 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
353	blk.29.attn_norm.weight	Block 29 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
354	blk.29.attn_output.weight	Block 29 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
355	blk.29.attn_q.weight	Block 29 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
356	blk.29.attn_q_norm.weight	Block 29 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
357	blk.29.attn_v.weight	Block 29 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
358	blk.29.ffn_down_exps.weight	Block 29 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
359	blk.29.ffn_gate_exps.weight	Block 29 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
360	blk.29.ffn_gate_inp.weight	Block 29 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
361	blk.29.ffn_norm.weight	Block 29 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
362	blk.29.ffn_up_exps.weight	Block 29 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.29: (~623M) 623120640
Percentage of total elements: 2.04%

Block 30 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
363	blk.30.attn_k.weight	Block 30 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
364	blk.30.attn_k_norm.weight	Block 30 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
365	blk.30.attn_norm.weight	Block 30 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
366	blk.30.attn_output.weight	Block 30 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
367	blk.30.attn_q.weight	Block 30 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
368	blk.30.attn_q_norm.weight	Block 30 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
369	blk.30.attn_v.weight	Block 30 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
370	blk.30.ffn_down_exps.weight	Block 30 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
371	blk.30.ffn_gate_exps.weight	Block 30 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
372	blk.30.ffn_gate_inp.weight	Block 30 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
373	blk.30.ffn_norm.weight	Block 30 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
374	blk.30.ffn_up_exps.weight	Block 30 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.30: (~623M) 623120640
Percentage of total elements: 2.04%

Block 31 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
375	blk.31.attn_k.weight	Block 31 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
376	blk.31.attn_k_norm.weight	Block 31 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
377	blk.31.attn_norm.weight	Block 31 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
378	blk.31.attn_output.weight	Block 31 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
379	blk.31.attn_q.weight	Block 31 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
380	blk.31.attn_q_norm.weight	Block 31 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
381	blk.31.attn_v.weight	Block 31 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
382	blk.31.ffn_down_exps.weight	Block 31 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
383	blk.31.ffn_gate_exps.weight	Block 31 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
384	blk.31.ffn_gate_inp.weight	Block 31 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
385	blk.31.ffn_norm.weight	Block 31 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
386	blk.31.ffn_up_exps.weight	Block 31 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.31: (~623M) 623120640
Percentage of total elements: 2.04%

Block 32 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
387	blk.32.attn_k.weight	Block 32 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
388	blk.32.attn_k_norm.weight	Block 32 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
389	blk.32.attn_norm.weight	Block 32 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
390	blk.32.attn_output.weight	Block 32 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
391	blk.32.attn_q.weight	Block 32 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
392	blk.32.attn_q_norm.weight	Block 32 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
393	blk.32.attn_v.weight	Block 32 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
394	blk.32.ffn_down_exps.weight	Block 32 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
395	blk.32.ffn_gate_exps.weight	Block 32 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
396	blk.32.ffn_gate_inp.weight	Block 32 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
397	blk.32.ffn_norm.weight	Block 32 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
398	blk.32.ffn_up_exps.weight	Block 32 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.32: (~623M) 623120640
Percentage of total elements: 2.04%

Block 33 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
399	blk.33.attn_k.weight	Block 33 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
400	blk.33.attn_k_norm.weight	Block 33 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
401	blk.33.attn_norm.weight	Block 33 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
402	blk.33.attn_output.weight	Block 33 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
403	blk.33.attn_q.weight	Block 33 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
404	blk.33.attn_q_norm.weight	Block 33 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
405	blk.33.attn_v.weight	Block 33 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
406	blk.33.ffn_down_exps.weight	Block 33 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
407	blk.33.ffn_gate_exps.weight	Block 33 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
408	blk.33.ffn_gate_inp.weight	Block 33 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
409	blk.33.ffn_norm.weight	Block 33 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
410	blk.33.ffn_up_exps.weight	Block 33 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.33: (~623M) 623120640
Percentage of total elements: 2.04%

Block 34 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
411	blk.34.attn_k.weight	Block 34 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
412	blk.34.attn_k_norm.weight	Block 34 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
413	blk.34.attn_norm.weight	Block 34 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
414	blk.34.attn_output.weight	Block 34 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
415	blk.34.attn_q.weight	Block 34 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
416	blk.34.attn_q_norm.weight	Block 34 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
417	blk.34.attn_v.weight	Block 34 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
418	blk.34.ffn_down_exps.weight	Block 34 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
419	blk.34.ffn_gate_exps.weight	Block 34 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
420	blk.34.ffn_gate_inp.weight	Block 34 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
421	blk.34.ffn_norm.weight	Block 34 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
422	blk.34.ffn_up_exps.weight	Block 34 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.34: (~623M) 623120640
Percentage of total elements: 2.04%

Block 35 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
423	blk.35.attn_k.weight	Block 35 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
424	blk.35.attn_k_norm.weight	Block 35 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
425	blk.35.attn_norm.weight	Block 35 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
426	blk.35.attn_output.weight	Block 35 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
427	blk.35.attn_q.weight	Block 35 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
428	blk.35.attn_q_norm.weight	Block 35 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
429	blk.35.attn_v.weight	Block 35 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
430	blk.35.ffn_down_exps.weight	Block 35 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
431	blk.35.ffn_gate_exps.weight	Block 35 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
432	blk.35.ffn_gate_inp.weight	Block 35 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
433	blk.35.ffn_norm.weight	Block 35 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
434	blk.35.ffn_up_exps.weight	Block 35 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.35: (~623M) 623120640
Percentage of total elements: 2.04%

Block 36 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
435	blk.36.attn_k.weight	Block 36 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
436	blk.36.attn_k_norm.weight	Block 36 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
437	blk.36.attn_norm.weight	Block 36 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
438	blk.36.attn_output.weight	Block 36 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
439	blk.36.attn_q.weight	Block 36 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
440	blk.36.attn_q_norm.weight	Block 36 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
441	blk.36.attn_v.weight	Block 36 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
442	blk.36.ffn_down_exps.weight	Block 36 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
443	blk.36.ffn_gate_exps.weight	Block 36 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
444	blk.36.ffn_gate_inp.weight	Block 36 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
445	blk.36.ffn_norm.weight	Block 36 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
446	blk.36.ffn_up_exps.weight	Block 36 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.36: (~623M) 623120640
Percentage of total elements: 2.04%

Block 37 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
447	blk.37.attn_k.weight	Block 37 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
448	blk.37.attn_k_norm.weight	Block 37 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
449	blk.37.attn_norm.weight	Block 37 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
450	blk.37.attn_output.weight	Block 37 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
451	blk.37.attn_q.weight	Block 37 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
452	blk.37.attn_q_norm.weight	Block 37 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
453	blk.37.attn_v.weight	Block 37 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
454	blk.37.ffn_down_exps.weight	Block 37 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
455	blk.37.ffn_gate_exps.weight	Block 37 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
456	blk.37.ffn_gate_inp.weight	Block 37 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
457	blk.37.ffn_norm.weight	Block 37 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
458	blk.37.ffn_up_exps.weight	Block 37 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.37: (~623M) 623120640
Percentage of total elements: 2.04%

Block 38 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
459	blk.38.attn_k.weight	Block 38 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
460	blk.38.attn_k_norm.weight	Block 38 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
461	blk.38.attn_norm.weight	Block 38 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
462	blk.38.attn_output.weight	Block 38 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
463	blk.38.attn_q.weight	Block 38 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
464	blk.38.attn_q_norm.weight	Block 38 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
465	blk.38.attn_v.weight	Block 38 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
466	blk.38.ffn_down_exps.weight	Block 38 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
467	blk.38.ffn_gate_exps.weight	Block 38 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
468	blk.38.ffn_gate_inp.weight	Block 38 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
469	blk.38.ffn_norm.weight	Block 38 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
470	blk.38.ffn_up_exps.weight	Block 38 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.38: (~623M) 623120640
Percentage of total elements: 2.04%

Block 39 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
471	blk.39.attn_k.weight	Block 39 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
472	blk.39.attn_k_norm.weight	Block 39 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
473	blk.39.attn_norm.weight	Block 39 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
474	blk.39.attn_output.weight	Block 39 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
475	blk.39.attn_q.weight	Block 39 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
476	blk.39.attn_q_norm.weight	Block 39 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
477	blk.39.attn_v.weight	Block 39 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
478	blk.39.ffn_down_exps.weight	Block 39 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
479	blk.39.ffn_gate_exps.weight	Block 39 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
480	blk.39.ffn_gate_inp.weight	Block 39 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
481	blk.39.ffn_norm.weight	Block 39 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
482	blk.39.ffn_up_exps.weight	Block 39 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.39: (~623M) 623120640
Percentage of total elements: 2.04%

Block 40 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
483	blk.40.attn_k.weight	Block 40 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
484	blk.40.attn_k_norm.weight	Block 40 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
485	blk.40.attn_norm.weight	Block 40 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
486	blk.40.attn_output.weight	Block 40 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
487	blk.40.attn_q.weight	Block 40 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
488	blk.40.attn_q_norm.weight	Block 40 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
489	blk.40.attn_v.weight	Block 40 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
490	blk.40.ffn_down_exps.weight	Block 40 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
491	blk.40.ffn_gate_exps.weight	Block 40 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
492	blk.40.ffn_gate_inp.weight	Block 40 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
493	blk.40.ffn_norm.weight	Block 40 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
494	blk.40.ffn_up_exps.weight	Block 40 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.40: (~623M) 623120640
Percentage of total elements: 2.04%

Block 41 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
495	blk.41.attn_k.weight	Block 41 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
496	blk.41.attn_k_norm.weight	Block 41 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
497	blk.41.attn_norm.weight	Block 41 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
498	blk.41.attn_output.weight	Block 41 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
499	blk.41.attn_q.weight	Block 41 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
500	blk.41.attn_q_norm.weight	Block 41 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
501	blk.41.attn_v.weight	Block 41 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
502	blk.41.ffn_down_exps.weight	Block 41 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
503	blk.41.ffn_gate_exps.weight	Block 41 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
504	blk.41.ffn_gate_inp.weight	Block 41 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
505	blk.41.ffn_norm.weight	Block 41 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
506	blk.41.ffn_up_exps.weight	Block 41 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.41: (~623M) 623120640
Percentage of total elements: 2.04%

Block 42 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
507	blk.42.attn_k.weight	Block 42 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
508	blk.42.attn_k_norm.weight	Block 42 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
509	blk.42.attn_norm.weight	Block 42 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
510	blk.42.attn_output.weight	Block 42 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
511	blk.42.attn_q.weight	Block 42 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
512	blk.42.attn_q_norm.weight	Block 42 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
513	blk.42.attn_v.weight	Block 42 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
514	blk.42.ffn_down_exps.weight	Block 42 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
515	blk.42.ffn_gate_exps.weight	Block 42 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
516	blk.42.ffn_gate_inp.weight	Block 42 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
517	blk.42.ffn_norm.weight	Block 42 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
518	blk.42.ffn_up_exps.weight	Block 42 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.42: (~623M) 623120640
Percentage of total elements: 2.04%

Block 43 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
519	blk.43.attn_k.weight	Block 43 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
520	blk.43.attn_k_norm.weight	Block 43 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
521	blk.43.attn_norm.weight	Block 43 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
522	blk.43.attn_output.weight	Block 43 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
523	blk.43.attn_q.weight	Block 43 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
524	blk.43.attn_q_norm.weight	Block 43 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
525	blk.43.attn_v.weight	Block 43 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
526	blk.43.ffn_down_exps.weight	Block 43 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
527	blk.43.ffn_gate_exps.weight	Block 43 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
528	blk.43.ffn_gate_inp.weight	Block 43 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
529	blk.43.ffn_norm.weight	Block 43 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
530	blk.43.ffn_up_exps.weight	Block 43 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.43: (~623M) 623120640
Percentage of total elements: 2.04%

Block 44 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
531	blk.44.attn_k.weight	Block 44 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
532	blk.44.attn_k_norm.weight	Block 44 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
533	blk.44.attn_norm.weight	Block 44 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
534	blk.44.attn_output.weight	Block 44 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
535	blk.44.attn_q.weight	Block 44 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
536	blk.44.attn_q_norm.weight	Block 44 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
537	blk.44.attn_v.weight	Block 44 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
538	blk.44.ffn_down_exps.weight	Block 44 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
539	blk.44.ffn_gate_exps.weight	Block 44 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
540	blk.44.ffn_gate_inp.weight	Block 44 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
541	blk.44.ffn_norm.weight	Block 44 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
542	blk.44.ffn_up_exps.weight	Block 44 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.44: (~623M) 623120640
Percentage of total elements: 2.04%

Block 45 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
543	blk.45.attn_k.weight	Block 45 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
544	blk.45.attn_k_norm.weight	Block 45 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
545	blk.45.attn_norm.weight	Block 45 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
546	blk.45.attn_output.weight	Block 45 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
547	blk.45.attn_q.weight	Block 45 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
548	blk.45.attn_q_norm.weight	Block 45 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
549	blk.45.attn_v.weight	Block 45 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
550	blk.45.ffn_down_exps.weight	Block 45 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
551	blk.45.ffn_gate_exps.weight	Block 45 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
552	blk.45.ffn_gate_inp.weight	Block 45 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
553	blk.45.ffn_norm.weight	Block 45 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
554	blk.45.ffn_up_exps.weight	Block 45 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.45: (~623M) 623120640
Percentage of total elements: 2.04%

Block 46 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
555	blk.46.attn_k.weight	Block 46 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
556	blk.46.attn_k_norm.weight	Block 46 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
557	blk.46.attn_norm.weight	Block 46 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
558	blk.46.attn_output.weight	Block 46 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
559	blk.46.attn_q.weight	Block 46 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
560	blk.46.attn_q_norm.weight	Block 46 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
561	blk.46.attn_v.weight	Block 46 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
562	blk.46.ffn_down_exps.weight	Block 46 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
563	blk.46.ffn_gate_exps.weight	Block 46 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
564	blk.46.ffn_gate_inp.weight	Block 46 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
565	blk.46.ffn_norm.weight	Block 46 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
566	blk.46.ffn_up_exps.weight	Block 46 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.46: (~623M) 623120640
Percentage of total elements: 2.04%

Block 47 Tensor Group : ~623M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type
567	blk.47.attn_k.weight	Block 47 Attention Key (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
568	blk.47.attn_k_norm.weight	Block 47 Attn_K_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
569	blk.47.attn_norm.weight	Block 47 Attention Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
570	blk.47.attn_output.weight	Block 47 Attention Output (W)	( ~8M) 8388608	4096 x 2048 x 1 x 1	Q3_K
571	blk.47.attn_q.weight	Block 47 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	Q3_K
572	blk.47.attn_q_norm.weight	Block 47 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
573	blk.47.attn_v.weight	Block 47 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	Q3_K
574	blk.47.ffn_down_exps.weight	Block 47 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	Q4_K
575	blk.47.ffn_gate_exps.weight	Block 47 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K
576	blk.47.ffn_gate_inp.weight	Block 47 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
577	blk.47.ffn_norm.weight	Block 47 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
578	blk.47.ffn_up_exps.weight	Block 47 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	Q3_K

Total elements in blk.47: (~623M) 623120640
Percentage of total elements: 2.04%