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-IQ3_M.md

eaddario

Add GGUF internal file structure

a630e18 verified about 1 year ago

preview code

raw

history blame

189 kB

Qwen3-30B-A3B-IQ3_M.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	27
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-IQ3_M.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)
0	output.weight	0x5b18c0	0x7f82800
1	output_norm.weight	0x85340c0	0x2000
2	token_embd.weight	0x85360c0	0x7f82800
3	blk.0.attn_k.weight	0x104b88c0	0x62000
4	blk.0.attn_k_norm.weight	0x1051a8c0	0x200
5	blk.0.attn_norm.weight	0x1051aac0	0x2000
6	blk.0.attn_output.weight	0x1051cac0	0x480000
7	blk.0.attn_q.weight	0x1099cac0	0x310000
8	blk.0.attn_q_norm.weight	0x10cacac0	0x200
9	blk.0.attn_v.weight	0x10caccc0	0x6e000
10	blk.0.ffn_down_exps.weight	0x10d1acc0	0x6c00000
11	blk.0.ffn_gate_exps.weight	0x1791acc0	0x4980000
12	blk.0.ffn_gate_inp.weight	0x1c29acc0	0x100000
13	blk.0.ffn_norm.weight	0x1c39acc0	0x2000
14	blk.0.ffn_up_exps.weight	0x1c39ccc0	0x4980000
15	blk.1.attn_k.weight	0x20d1ccc0	0x62000
16	blk.1.attn_k_norm.weight	0x20d7ecc0	0x200
17	blk.1.attn_norm.weight	0x20d7eec0	0x2000
18	blk.1.attn_output.weight	0x20d80ec0	0x480000
19	blk.1.attn_q.weight	0x21200ec0	0x310000
20	blk.1.attn_q_norm.weight	0x21510ec0	0x200
21	blk.1.attn_v.weight	0x215110c0	0x6e000
22	blk.1.ffn_down_exps.weight	0x2157f0c0	0x6c00000
23	blk.1.ffn_gate_exps.weight	0x2817f0c0	0x4980000
24	blk.1.ffn_gate_inp.weight	0x2caff0c0	0x100000
25	blk.1.ffn_norm.weight	0x2cbff0c0	0x2000
26	blk.1.ffn_up_exps.weight	0x2cc010c0	0x4980000
27	blk.2.attn_k.weight	0x315810c0	0x62000
28	blk.2.attn_k_norm.weight	0x315e30c0	0x200
29	blk.2.attn_norm.weight	0x315e32c0	0x2000
30	blk.2.attn_output.weight	0x315e52c0	0x480000
31	blk.2.attn_q.weight	0x31a652c0	0x310000
32	blk.2.attn_q_norm.weight	0x31d752c0	0x200
33	blk.2.attn_v.weight	0x31d754c0	0x6e000
34	blk.2.ffn_down_exps.weight	0x31de34c0	0x6c00000
35	blk.2.ffn_gate_exps.weight	0x389e34c0	0x4980000
36	blk.2.ffn_gate_inp.weight	0x3d3634c0	0x100000
37	blk.2.ffn_norm.weight	0x3d4634c0	0x2000
38	blk.2.ffn_up_exps.weight	0x3d4654c0	0x4980000
39	blk.3.attn_k.weight	0x41de54c0	0x62000
40	blk.3.attn_k_norm.weight	0x41e474c0	0x200
41	blk.3.attn_norm.weight	0x41e476c0	0x2000
42	blk.3.attn_output.weight	0x41e496c0	0x480000
43	blk.3.attn_q.weight	0x422c96c0	0x310000
44	blk.3.attn_q_norm.weight	0x425d96c0	0x200
45	blk.3.attn_v.weight	0x425d98c0	0x6e000
46	blk.3.ffn_down_exps.weight	0x426478c0	0x6c00000
47	blk.3.ffn_gate_exps.weight	0x492478c0	0x4980000
48	blk.3.ffn_gate_inp.weight	0x4dbc78c0	0x100000
49	blk.3.ffn_norm.weight	0x4dcc78c0	0x2000
50	blk.3.ffn_up_exps.weight	0x4dcc98c0	0x4980000
51	blk.4.attn_k.weight	0x526498c0	0x62000
52	blk.4.attn_k_norm.weight	0x526ab8c0	0x200
53	blk.4.attn_norm.weight	0x526abac0	0x2000
54	blk.4.attn_output.weight	0x526adac0	0x480000
55	blk.4.attn_q.weight	0x52b2dac0	0x310000
56	blk.4.attn_q_norm.weight	0x52e3dac0	0x200
57	blk.4.attn_v.weight	0x52e3dcc0	0x6e000
58	blk.4.ffn_down_exps.weight	0x52eabcc0	0x6c00000
59	blk.4.ffn_gate_exps.weight	0x59aabcc0	0x4980000
60	blk.4.ffn_gate_inp.weight	0x5e42bcc0	0x100000
61	blk.4.ffn_norm.weight	0x5e52bcc0	0x2000
62	blk.4.ffn_up_exps.weight	0x5e52dcc0	0x4980000
63	blk.5.attn_k.weight	0x62eadcc0	0x62000
64	blk.5.attn_k_norm.weight	0x62f0fcc0	0x200
65	blk.5.attn_norm.weight	0x62f0fec0	0x2000
66	blk.5.attn_output.weight	0x62f11ec0	0x480000
67	blk.5.attn_q.weight	0x63391ec0	0x310000
68	blk.5.attn_q_norm.weight	0x636a1ec0	0x200
69	blk.5.attn_v.weight	0x636a20c0	0x6e000
70	blk.5.ffn_down_exps.weight	0x637100c0	0x6c00000
71	blk.5.ffn_gate_exps.weight	0x6a3100c0	0x4980000
72	blk.5.ffn_gate_inp.weight	0x6ec900c0	0x100000
73	blk.5.ffn_norm.weight	0x6ed900c0	0x2000
74	blk.5.ffn_up_exps.weight	0x6ed920c0	0x4980000
75	blk.6.attn_k.weight	0x737120c0	0x62000
76	blk.6.attn_k_norm.weight	0x737740c0	0x200
77	blk.6.attn_norm.weight	0x737742c0	0x2000
78	blk.6.attn_output.weight	0x737762c0	0x480000
79	blk.6.attn_q.weight	0x73bf62c0	0x310000
80	blk.6.attn_q_norm.weight	0x73f062c0	0x200
81	blk.6.attn_v.weight	0x73f064c0	0x6e000
82	blk.6.ffn_down_exps.weight	0x73f744c0	0x6c00000
83	blk.6.ffn_gate_exps.weight	0x7ab744c0	0x4980000
84	blk.6.ffn_gate_inp.weight	0x7f4f44c0	0x100000
85	blk.6.ffn_norm.weight	0x7f5f44c0	0x2000
86	blk.6.ffn_up_exps.weight	0x7f5f64c0	0x4980000
87	blk.7.attn_k.weight	0x83f764c0	0x62000
88	blk.7.attn_k_norm.weight	0x83fd84c0	0x200
89	blk.7.attn_norm.weight	0x83fd86c0	0x2000
90	blk.7.attn_output.weight	0x83fda6c0	0x480000
91	blk.7.attn_q.weight	0x8445a6c0	0x310000
92	blk.7.attn_q_norm.weight	0x8476a6c0	0x200
93	blk.7.attn_v.weight	0x8476a8c0	0x6e000
94	blk.7.ffn_down_exps.weight	0x847d88c0	0x6c00000
95	blk.7.ffn_gate_exps.weight	0x8b3d88c0	0x4980000
96	blk.7.ffn_gate_inp.weight	0x8fd588c0	0x100000
97	blk.7.ffn_norm.weight	0x8fe588c0	0x2000
98	blk.7.ffn_up_exps.weight	0x8fe5a8c0	0x4980000
99	blk.8.attn_k.weight	0x947da8c0	0x62000
100	blk.8.attn_k_norm.weight	0x9483c8c0	0x200
101	blk.8.attn_norm.weight	0x9483cac0	0x2000
102	blk.8.attn_output.weight	0x9483eac0	0x480000
103	blk.8.attn_q.weight	0x94cbeac0	0x310000
104	blk.8.attn_q_norm.weight	0x94fceac0	0x200
105	blk.8.attn_v.weight	0x94fcecc0	0x6e000
106	blk.8.ffn_down_exps.weight	0x9503ccc0	0x6c00000
107	blk.8.ffn_gate_exps.weight	0x9bc3ccc0	0x4980000
108	blk.8.ffn_gate_inp.weight	0xa05bccc0	0x100000
109	blk.8.ffn_norm.weight	0xa06bccc0	0x2000
110	blk.8.ffn_up_exps.weight	0xa06becc0	0x4980000
111	blk.9.attn_k.weight	0xa503ecc0	0x62000
112	blk.9.attn_k_norm.weight	0xa50a0cc0	0x200
113	blk.9.attn_norm.weight	0xa50a0ec0	0x2000
114	blk.9.attn_output.weight	0xa50a2ec0	0x480000
115	blk.9.attn_q.weight	0xa5522ec0	0x310000
116	blk.9.attn_q_norm.weight	0xa5832ec0	0x200
117	blk.9.attn_v.weight	0xa58330c0	0x6e000
118	blk.9.ffn_down_exps.weight	0xa58a10c0	0x6c00000
119	blk.9.ffn_gate_exps.weight	0xac4a10c0	0x4980000
120	blk.9.ffn_gate_inp.weight	0xb0e210c0	0x100000
121	blk.9.ffn_norm.weight	0xb0f210c0	0x2000
122	blk.9.ffn_up_exps.weight	0xb0f230c0	0x4980000
123	blk.10.attn_k.weight	0xb58a30c0	0x62000
124	blk.10.attn_k_norm.weight	0xb59050c0	0x200
125	blk.10.attn_norm.weight	0xb59052c0	0x2000
126	blk.10.attn_output.weight	0xb59072c0	0x480000
127	blk.10.attn_q.weight	0xb5d872c0	0x310000
128	blk.10.attn_q_norm.weight	0xb60972c0	0x200
129	blk.10.attn_v.weight	0xb60974c0	0x6e000
130	blk.10.ffn_down_exps.weight	0xb61054c0	0x6c00000
131	blk.10.ffn_gate_exps.weight	0xbcd054c0	0x4980000
132	blk.10.ffn_gate_inp.weight	0xc16854c0	0x100000
133	blk.10.ffn_norm.weight	0xc17854c0	0x2000
134	blk.10.ffn_up_exps.weight	0xc17874c0	0x4980000
135	blk.11.attn_k.weight	0xc61074c0	0x62000
136	blk.11.attn_k_norm.weight	0xc61694c0	0x200
137	blk.11.attn_norm.weight	0xc61696c0	0x2000
138	blk.11.attn_output.weight	0xc616b6c0	0x480000
139	blk.11.attn_q.weight	0xc65eb6c0	0x310000
140	blk.11.attn_q_norm.weight	0xc68fb6c0	0x200
141	blk.11.attn_v.weight	0xc68fb8c0	0x6e000
142	blk.11.ffn_down_exps.weight	0xc69698c0	0x6c00000
143	blk.11.ffn_gate_exps.weight	0xcd5698c0	0x4980000
144	blk.11.ffn_gate_inp.weight	0xd1ee98c0	0x100000
145	blk.11.ffn_norm.weight	0xd1fe98c0	0x2000
146	blk.11.ffn_up_exps.weight	0xd1feb8c0	0x4980000
147	blk.12.attn_k.weight	0xd696b8c0	0x62000
148	blk.12.attn_k_norm.weight	0xd69cd8c0	0x200
149	blk.12.attn_norm.weight	0xd69cdac0	0x2000
150	blk.12.attn_output.weight	0xd69cfac0	0x480000
151	blk.12.attn_q.weight	0xd6e4fac0	0x310000
152	blk.12.attn_q_norm.weight	0xd715fac0	0x200
153	blk.12.attn_v.weight	0xd715fcc0	0x6e000
154	blk.12.ffn_down_exps.weight	0xd71cdcc0	0x6c00000
155	blk.12.ffn_gate_exps.weight	0xdddcdcc0	0x4980000
156	blk.12.ffn_gate_inp.weight	0xe274dcc0	0x100000
157	blk.12.ffn_norm.weight	0xe284dcc0	0x2000
158	blk.12.ffn_up_exps.weight	0xe284fcc0	0x4980000
159	blk.13.attn_k.weight	0xe71cfcc0	0x62000
160	blk.13.attn_k_norm.weight	0xe7231cc0	0x200
161	blk.13.attn_norm.weight	0xe7231ec0	0x2000
162	blk.13.attn_output.weight	0xe7233ec0	0x480000
163	blk.13.attn_q.weight	0xe76b3ec0	0x310000
164	blk.13.attn_q_norm.weight	0xe79c3ec0	0x200
165	blk.13.attn_v.weight	0xe79c40c0	0x6e000
166	blk.13.ffn_down_exps.weight	0xe7a320c0	0x6c00000
167	blk.13.ffn_gate_exps.weight	0xee6320c0	0x5280000
168	blk.13.ffn_gate_inp.weight	0xf38b20c0	0x100000
169	blk.13.ffn_norm.weight	0xf39b20c0	0x2000
170	blk.13.ffn_up_exps.weight	0xf39b40c0	0x5280000
171	blk.14.attn_k.weight	0xf8c340c0	0x62000
172	blk.14.attn_k_norm.weight	0xf8c960c0	0x200
173	blk.14.attn_norm.weight	0xf8c962c0	0x2000
174	blk.14.attn_output.weight	0xf8c982c0	0x480000
175	blk.14.attn_q.weight	0xf91182c0	0x310000
176	blk.14.attn_q_norm.weight	0xf94282c0	0x200
177	blk.14.attn_v.weight	0xf94284c0	0x6e000
178	blk.14.ffn_down_exps.weight	0xf94964c0	0x6c00000
179	blk.14.ffn_gate_exps.weight	0x1000964c0	0x4980000
180	blk.14.ffn_gate_inp.weight	0x104a164c0	0x100000
181	blk.14.ffn_norm.weight	0x104b164c0	0x2000
182	blk.14.ffn_up_exps.weight	0x104b184c0	0x4980000
183	blk.15.attn_k.weight	0x1094984c0	0x62000
184	blk.15.attn_k_norm.weight	0x1094fa4c0	0x200
185	blk.15.attn_norm.weight	0x1094fa6c0	0x2000
186	blk.15.attn_output.weight	0x1094fc6c0	0x480000
187	blk.15.attn_q.weight	0x10997c6c0	0x310000
188	blk.15.attn_q_norm.weight	0x109c8c6c0	0x200
189	blk.15.attn_v.weight	0x109c8c8c0	0x6e000
190	blk.15.ffn_down_exps.weight	0x109cfa8c0	0x6c00000
191	blk.15.ffn_gate_exps.weight	0x1108fa8c0	0x5280000
192	blk.15.ffn_gate_inp.weight	0x115b7a8c0	0x100000
193	blk.15.ffn_norm.weight	0x115c7a8c0	0x2000
194	blk.15.ffn_up_exps.weight	0x115c7c8c0	0x5280000
195	blk.16.attn_k.weight	0x11aefc8c0	0x62000
196	blk.16.attn_k_norm.weight	0x11af5e8c0	0x200
197	blk.16.attn_norm.weight	0x11af5eac0	0x2000
198	blk.16.attn_output.weight	0x11af60ac0	0x480000
199	blk.16.attn_q.weight	0x11b3e0ac0	0x310000
200	blk.16.attn_q_norm.weight	0x11b6f0ac0	0x200
201	blk.16.attn_v.weight	0x11b6f0cc0	0x6e000
202	blk.16.ffn_down_exps.weight	0x11b75ecc0	0x6c00000
203	blk.16.ffn_gate_exps.weight	0x12235ecc0	0x4980000
204	blk.16.ffn_gate_inp.weight	0x126cdecc0	0x100000
205	blk.16.ffn_norm.weight	0x126ddecc0	0x2000
206	blk.16.ffn_up_exps.weight	0x126de0cc0	0x4980000
207	blk.17.attn_k.weight	0x12b760cc0	0x62000
208	blk.17.attn_k_norm.weight	0x12b7c2cc0	0x200
209	blk.17.attn_norm.weight	0x12b7c2ec0	0x2000
210	blk.17.attn_output.weight	0x12b7c4ec0	0x480000
211	blk.17.attn_q.weight	0x12bc44ec0	0x310000
212	blk.17.attn_q_norm.weight	0x12bf54ec0	0x200
213	blk.17.attn_v.weight	0x12bf550c0	0x6e000
214	blk.17.ffn_down_exps.weight	0x12bfc30c0	0x6c00000
215	blk.17.ffn_gate_exps.weight	0x132bc30c0	0x4980000
216	blk.17.ffn_gate_inp.weight	0x1375430c0	0x100000
217	blk.17.ffn_norm.weight	0x1376430c0	0x2000
218	blk.17.ffn_up_exps.weight	0x1376450c0	0x4980000
219	blk.18.attn_k.weight	0x13bfc50c0	0x62000
220	blk.18.attn_k_norm.weight	0x13c0270c0	0x200
221	blk.18.attn_norm.weight	0x13c0272c0	0x2000
222	blk.18.attn_output.weight	0x13c0292c0	0x480000
223	blk.18.attn_q.weight	0x13c4a92c0	0x310000
224	blk.18.attn_q_norm.weight	0x13c7b92c0	0x200
225	blk.18.attn_v.weight	0x13c7b94c0	0x6e000
226	blk.18.ffn_down_exps.weight	0x13c8274c0	0x6c00000
227	blk.18.ffn_gate_exps.weight	0x1434274c0	0x4980000
228	blk.18.ffn_gate_inp.weight	0x147da74c0	0x100000
229	blk.18.ffn_norm.weight	0x147ea74c0	0x2000
230	blk.18.ffn_up_exps.weight	0x147ea94c0	0x4980000
231	blk.19.attn_k.weight	0x14c8294c0	0x62000
232	blk.19.attn_k_norm.weight	0x14c88b4c0	0x200
233	blk.19.attn_norm.weight	0x14c88b6c0	0x2000
234	blk.19.attn_output.weight	0x14c88d6c0	0x480000
235	blk.19.attn_q.weight	0x14cd0d6c0	0x310000
236	blk.19.attn_q_norm.weight	0x14d01d6c0	0x200
237	blk.19.attn_v.weight	0x14d01d8c0	0x6e000
238	blk.19.ffn_down_exps.weight	0x14d08b8c0	0x6c00000
239	blk.19.ffn_gate_exps.weight	0x153c8b8c0	0x4980000
240	blk.19.ffn_gate_inp.weight	0x15860b8c0	0x100000
241	blk.19.ffn_norm.weight	0x15870b8c0	0x2000
242	blk.19.ffn_up_exps.weight	0x15870d8c0	0x4980000
243	blk.20.attn_k.weight	0x15d08d8c0	0x62000
244	blk.20.attn_k_norm.weight	0x15d0ef8c0	0x200
245	blk.20.attn_norm.weight	0x15d0efac0	0x2000
246	blk.20.attn_output.weight	0x15d0f1ac0	0x480000
247	blk.20.attn_q.weight	0x15d571ac0	0x310000
248	blk.20.attn_q_norm.weight	0x15d881ac0	0x200
249	blk.20.attn_v.weight	0x15d881cc0	0x6e000
250	blk.20.ffn_down_exps.weight	0x15d8efcc0	0x6c00000
251	blk.20.ffn_gate_exps.weight	0x1644efcc0	0x4980000
252	blk.20.ffn_gate_inp.weight	0x168e6fcc0	0x100000
253	blk.20.ffn_norm.weight	0x168f6fcc0	0x2000
254	blk.20.ffn_up_exps.weight	0x168f71cc0	0x4980000
255	blk.21.attn_k.weight	0x16d8f1cc0	0x62000
256	blk.21.attn_k_norm.weight	0x16d953cc0	0x200
257	blk.21.attn_norm.weight	0x16d953ec0	0x2000
258	blk.21.attn_output.weight	0x16d955ec0	0x480000
259	blk.21.attn_q.weight	0x16ddd5ec0	0x310000
260	blk.21.attn_q_norm.weight	0x16e0e5ec0	0x200
261	blk.21.attn_v.weight	0x16e0e60c0	0x6e000
262	blk.21.ffn_down_exps.weight	0x16e1540c0	0x6c00000
263	blk.21.ffn_gate_exps.weight	0x174d540c0	0x4980000
264	blk.21.ffn_gate_inp.weight	0x1796d40c0	0x100000
265	blk.21.ffn_norm.weight	0x1797d40c0	0x2000
266	blk.21.ffn_up_exps.weight	0x1797d60c0	0x4980000
267	blk.22.attn_k.weight	0x17e1560c0	0x62000
268	blk.22.attn_k_norm.weight	0x17e1b80c0	0x200
269	blk.22.attn_norm.weight	0x17e1b82c0	0x2000
270	blk.22.attn_output.weight	0x17e1ba2c0	0x480000
271	blk.22.attn_q.weight	0x17e63a2c0	0x310000
272	blk.22.attn_q_norm.weight	0x17e94a2c0	0x200
273	blk.22.attn_v.weight	0x17e94a4c0	0x6e000
274	blk.22.ffn_down_exps.weight	0x17e9b84c0	0x6c00000
275	blk.22.ffn_gate_exps.weight	0x1855b84c0	0x4980000
276	blk.22.ffn_gate_inp.weight	0x189f384c0	0x100000
277	blk.22.ffn_norm.weight	0x18a0384c0	0x2000
278	blk.22.ffn_up_exps.weight	0x18a03a4c0	0x4980000
279	blk.23.attn_k.weight	0x18e9ba4c0	0x62000
280	blk.23.attn_k_norm.weight	0x18ea1c4c0	0x200
281	blk.23.attn_norm.weight	0x18ea1c6c0	0x2000
282	blk.23.attn_output.weight	0x18ea1e6c0	0x480000
283	blk.23.attn_q.weight	0x18ee9e6c0	0x310000
284	blk.23.attn_q_norm.weight	0x18f1ae6c0	0x200
285	blk.23.attn_v.weight	0x18f1ae8c0	0x6e000
286	blk.23.ffn_down_exps.weight	0x18f21c8c0	0x6c00000
287	blk.23.ffn_gate_exps.weight	0x195e1c8c0	0x4980000
288	blk.23.ffn_gate_inp.weight	0x19a79c8c0	0x100000
289	blk.23.ffn_norm.weight	0x19a89c8c0	0x2000
290	blk.23.ffn_up_exps.weight	0x19a89e8c0	0x4980000
291	blk.24.attn_k.weight	0x19f21e8c0	0x6e000
292	blk.24.attn_k_norm.weight	0x19f28c8c0	0x200
293	blk.24.attn_norm.weight	0x19f28cac0	0x2000
294	blk.24.attn_output.weight	0x19f28eac0	0x480000
295	blk.24.attn_q.weight	0x19f70eac0	0x370000
296	blk.24.attn_q_norm.weight	0x19fa7eac0	0x200
297	blk.24.attn_v.weight	0x19fa7ecc0	0x90000
298	blk.24.ffn_down_exps.weight	0x19fb0ecc0	0x6c00000
299	blk.24.ffn_gate_exps.weight	0x1a670ecc0	0x4980000
300	blk.24.ffn_gate_inp.weight	0x1ab08ecc0	0x100000
301	blk.24.ffn_norm.weight	0x1ab18ecc0	0x2000
302	blk.24.ffn_up_exps.weight	0x1ab190cc0	0x4980000
303	blk.25.attn_k.weight	0x1afb10cc0	0x6e000
304	blk.25.attn_k_norm.weight	0x1afb7ecc0	0x200
305	blk.25.attn_norm.weight	0x1afb7eec0	0x2000
306	blk.25.attn_output.weight	0x1afb80ec0	0x480000
307	blk.25.attn_q.weight	0x1b0000ec0	0x370000
308	blk.25.attn_q_norm.weight	0x1b0370ec0	0x200
309	blk.25.attn_v.weight	0x1b03710c0	0x90000
310	blk.25.ffn_down_exps.weight	0x1b04010c0	0x6c00000
311	blk.25.ffn_gate_exps.weight	0x1b70010c0	0x5280000
312	blk.25.ffn_gate_inp.weight	0x1bc2810c0	0x100000
313	blk.25.ffn_norm.weight	0x1bc3810c0	0x2000
314	blk.25.ffn_up_exps.weight	0x1bc3830c0	0x5280000
315	blk.26.attn_k.weight	0x1c16030c0	0x6e000
316	blk.26.attn_k_norm.weight	0x1c16710c0	0x200
317	blk.26.attn_norm.weight	0x1c16712c0	0x2000
318	blk.26.attn_output.weight	0x1c16732c0	0x480000
319	blk.26.attn_q.weight	0x1c1af32c0	0x370000
320	blk.26.attn_q_norm.weight	0x1c1e632c0	0x200
321	blk.26.attn_v.weight	0x1c1e634c0	0x90000
322	blk.26.ffn_down_exps.weight	0x1c1ef34c0	0x6c00000
323	blk.26.ffn_gate_exps.weight	0x1c8af34c0	0x4980000
324	blk.26.ffn_gate_inp.weight	0x1cd4734c0	0x100000
325	blk.26.ffn_norm.weight	0x1cd5734c0	0x2000
326	blk.26.ffn_up_exps.weight	0x1cd5754c0	0x4980000
327	blk.27.attn_k.weight	0x1d1ef54c0	0x6e000
328	blk.27.attn_k_norm.weight	0x1d1f634c0	0x200
329	blk.27.attn_norm.weight	0x1d1f636c0	0x2000
330	blk.27.attn_output.weight	0x1d1f656c0	0x480000
331	blk.27.attn_q.weight	0x1d23e56c0	0x370000
332	blk.27.attn_q_norm.weight	0x1d27556c0	0x200
333	blk.27.attn_v.weight	0x1d27558c0	0x90000
334	blk.27.ffn_down_exps.weight	0x1d27e58c0	0x6c00000
335	blk.27.ffn_gate_exps.weight	0x1d93e58c0	0x5280000
336	blk.27.ffn_gate_inp.weight	0x1de6658c0	0x100000
337	blk.27.ffn_norm.weight	0x1de7658c0	0x2000
338	blk.27.ffn_up_exps.weight	0x1de7678c0	0x5280000
339	blk.28.attn_k.weight	0x1e39e78c0	0x6e000
340	blk.28.attn_k_norm.weight	0x1e3a558c0	0x200
341	blk.28.attn_norm.weight	0x1e3a55ac0	0x2000
342	blk.28.attn_output.weight	0x1e3a57ac0	0x480000
343	blk.28.attn_q.weight	0x1e3ed7ac0	0x370000
344	blk.28.attn_q_norm.weight	0x1e4247ac0	0x200
345	blk.28.attn_v.weight	0x1e4247cc0	0x90000
346	blk.28.ffn_down_exps.weight	0x1e42d7cc0	0x6c00000
347	blk.28.ffn_gate_exps.weight	0x1eaed7cc0	0x5280000
348	blk.28.ffn_gate_inp.weight	0x1f0157cc0	0x100000
349	blk.28.ffn_norm.weight	0x1f0257cc0	0x2000
350	blk.28.ffn_up_exps.weight	0x1f0259cc0	0x5280000
351	blk.29.attn_k.weight	0x1f54d9cc0	0x6e000
352	blk.29.attn_k_norm.weight	0x1f5547cc0	0x200
353	blk.29.attn_norm.weight	0x1f5547ec0	0x2000
354	blk.29.attn_output.weight	0x1f5549ec0	0x480000
355	blk.29.attn_q.weight	0x1f59c9ec0	0x370000
356	blk.29.attn_q_norm.weight	0x1f5d39ec0	0x200
357	blk.29.attn_v.weight	0x1f5d3a0c0	0x90000
358	blk.29.ffn_down_exps.weight	0x1f5dca0c0	0x6c00000
359	blk.29.ffn_gate_exps.weight	0x1fc9ca0c0	0x5280000
360	blk.29.ffn_gate_inp.weight	0x201c4a0c0	0x100000
361	blk.29.ffn_norm.weight	0x201d4a0c0	0x2000
362	blk.29.ffn_up_exps.weight	0x201d4c0c0	0x5280000
363	blk.30.attn_k.weight	0x206fcc0c0	0x6e000
364	blk.30.attn_k_norm.weight	0x20703a0c0	0x200
365	blk.30.attn_norm.weight	0x20703a2c0	0x2000
366	blk.30.attn_output.weight	0x20703c2c0	0x480000
367	blk.30.attn_q.weight	0x2074bc2c0	0x370000
368	blk.30.attn_q_norm.weight	0x20782c2c0	0x200
369	blk.30.attn_v.weight	0x20782c4c0	0x90000
370	blk.30.ffn_down_exps.weight	0x2078bc4c0	0x6c00000
371	blk.30.ffn_gate_exps.weight	0x20e4bc4c0	0x5280000
372	blk.30.ffn_gate_inp.weight	0x21373c4c0	0x100000
373	blk.30.ffn_norm.weight	0x21383c4c0	0x2000
374	blk.30.ffn_up_exps.weight	0x21383e4c0	0x5280000
375	blk.31.attn_k.weight	0x218abe4c0	0x6e000
376	blk.31.attn_k_norm.weight	0x218b2c4c0	0x200
377	blk.31.attn_norm.weight	0x218b2c6c0	0x2000
378	blk.31.attn_output.weight	0x218b2e6c0	0x480000
379	blk.31.attn_q.weight	0x218fae6c0	0x370000
380	blk.31.attn_q_norm.weight	0x21931e6c0	0x200
381	blk.31.attn_v.weight	0x21931e8c0	0x90000
382	blk.31.ffn_down_exps.weight	0x2193ae8c0	0x6c00000
383	blk.31.ffn_gate_exps.weight	0x21ffae8c0	0x5280000
384	blk.31.ffn_gate_inp.weight	0x22522e8c0	0x100000
385	blk.31.ffn_norm.weight	0x22532e8c0	0x2000
386	blk.31.ffn_up_exps.weight	0x2253308c0	0x5280000
387	blk.32.attn_k.weight	0x22a5b08c0	0x6e000
388	blk.32.attn_k_norm.weight	0x22a61e8c0	0x200
389	blk.32.attn_norm.weight	0x22a61eac0	0x2000
390	blk.32.attn_output.weight	0x22a620ac0	0x480000
391	blk.32.attn_q.weight	0x22aaa0ac0	0x370000
392	blk.32.attn_q_norm.weight	0x22ae10ac0	0x200
393	blk.32.attn_v.weight	0x22ae10cc0	0x90000
394	blk.32.ffn_down_exps.weight	0x22aea0cc0	0x6c00000
395	blk.32.ffn_gate_exps.weight	0x231aa0cc0	0x5280000
396	blk.32.ffn_gate_inp.weight	0x236d20cc0	0x100000
397	blk.32.ffn_norm.weight	0x236e20cc0	0x2000
398	blk.32.ffn_up_exps.weight	0x236e22cc0	0x5280000
399	blk.33.attn_k.weight	0x23c0a2cc0	0x6e000
400	blk.33.attn_k_norm.weight	0x23c110cc0	0x200
401	blk.33.attn_norm.weight	0x23c110ec0	0x2000
402	blk.33.attn_output.weight	0x23c112ec0	0x480000
403	blk.33.attn_q.weight	0x23c592ec0	0x370000
404	blk.33.attn_q_norm.weight	0x23c902ec0	0x200
405	blk.33.attn_v.weight	0x23c9030c0	0x90000
406	blk.33.ffn_down_exps.weight	0x23c9930c0	0x6c00000
407	blk.33.ffn_gate_exps.weight	0x2435930c0	0x5280000
408	blk.33.ffn_gate_inp.weight	0x2488130c0	0x100000
409	blk.33.ffn_norm.weight	0x2489130c0	0x2000
410	blk.33.ffn_up_exps.weight	0x2489150c0	0x5280000
411	blk.34.attn_k.weight	0x24db950c0	0x6e000
412	blk.34.attn_k_norm.weight	0x24dc030c0	0x200
413	blk.34.attn_norm.weight	0x24dc032c0	0x2000
414	blk.34.attn_output.weight	0x24dc052c0	0x480000
415	blk.34.attn_q.weight	0x24e0852c0	0x370000
416	blk.34.attn_q_norm.weight	0x24e3f52c0	0x200
417	blk.34.attn_v.weight	0x24e3f54c0	0x90000
418	blk.34.ffn_down_exps.weight	0x24e4854c0	0x6c00000
419	blk.34.ffn_gate_exps.weight	0x2550854c0	0x5280000
420	blk.34.ffn_gate_inp.weight	0x25a3054c0	0x100000
421	blk.34.ffn_norm.weight	0x25a4054c0	0x2000
422	blk.34.ffn_up_exps.weight	0x25a4074c0	0x5280000
423	blk.35.attn_k.weight	0x25f6874c0	0x6e000
424	blk.35.attn_k_norm.weight	0x25f6f54c0	0x200
425	blk.35.attn_norm.weight	0x25f6f56c0	0x2000
426	blk.35.attn_output.weight	0x25f6f76c0	0x480000
427	blk.35.attn_q.weight	0x25fb776c0	0x370000
428	blk.35.attn_q_norm.weight	0x25fee76c0	0x200
429	blk.35.attn_v.weight	0x25fee78c0	0x90000
430	blk.35.ffn_down_exps.weight	0x25ff778c0	0x6c00000
431	blk.35.ffn_gate_exps.weight	0x266b778c0	0x5280000
432	blk.35.ffn_gate_inp.weight	0x26bdf78c0	0x100000
433	blk.35.ffn_norm.weight	0x26bef78c0	0x2000
434	blk.35.ffn_up_exps.weight	0x26bef98c0	0x5280000
435	blk.36.attn_k.weight	0x2711798c0	0x6e000
436	blk.36.attn_k_norm.weight	0x2711e78c0	0x200
437	blk.36.attn_norm.weight	0x2711e7ac0	0x2000
438	blk.36.attn_output.weight	0x2711e9ac0	0x480000
439	blk.36.attn_q.weight	0x271669ac0	0x370000
440	blk.36.attn_q_norm.weight	0x2719d9ac0	0x200
441	blk.36.attn_v.weight	0x2719d9cc0	0x90000
442	blk.36.ffn_down_exps.weight	0x271a69cc0	0x6c00000
443	blk.36.ffn_gate_exps.weight	0x278669cc0	0x5280000
444	blk.36.ffn_gate_inp.weight	0x27d8e9cc0	0x100000
445	blk.36.ffn_norm.weight	0x27d9e9cc0	0x2000
446	blk.36.ffn_up_exps.weight	0x27d9ebcc0	0x5280000
447	blk.37.attn_k.weight	0x282c6bcc0	0x6e000
448	blk.37.attn_k_norm.weight	0x282cd9cc0	0x200
449	blk.37.attn_norm.weight	0x282cd9ec0	0x2000
450	blk.37.attn_output.weight	0x282cdbec0	0x480000
451	blk.37.attn_q.weight	0x28315bec0	0x370000
452	blk.37.attn_q_norm.weight	0x2834cbec0	0x200
453	blk.37.attn_v.weight	0x2834cc0c0	0x90000
454	blk.37.ffn_down_exps.weight	0x28355c0c0	0x6c00000
455	blk.37.ffn_gate_exps.weight	0x28a15c0c0	0x5280000
456	blk.37.ffn_gate_inp.weight	0x28f3dc0c0	0x100000
457	blk.37.ffn_norm.weight	0x28f4dc0c0	0x2000
458	blk.37.ffn_up_exps.weight	0x28f4de0c0	0x5280000
459	blk.38.attn_k.weight	0x29475e0c0	0x6e000
460	blk.38.attn_k_norm.weight	0x2947cc0c0	0x200
461	blk.38.attn_norm.weight	0x2947cc2c0	0x2000
462	blk.38.attn_output.weight	0x2947ce2c0	0x480000
463	blk.38.attn_q.weight	0x294c4e2c0	0x370000
464	blk.38.attn_q_norm.weight	0x294fbe2c0	0x200
465	blk.38.attn_v.weight	0x294fbe4c0	0x90000
466	blk.38.ffn_down_exps.weight	0x29504e4c0	0x6c00000
467	blk.38.ffn_gate_exps.weight	0x29bc4e4c0	0x5280000
468	blk.38.ffn_gate_inp.weight	0x2a0ece4c0	0x100000
469	blk.38.ffn_norm.weight	0x2a0fce4c0	0x2000
470	blk.38.ffn_up_exps.weight	0x2a0fd04c0	0x5280000
471	blk.39.attn_k.weight	0x2a62504c0	0x6e000
472	blk.39.attn_k_norm.weight	0x2a62be4c0	0x200
473	blk.39.attn_norm.weight	0x2a62be6c0	0x2000
474	blk.39.attn_output.weight	0x2a62c06c0	0x480000
475	blk.39.attn_q.weight	0x2a67406c0	0x370000
476	blk.39.attn_q_norm.weight	0x2a6ab06c0	0x200
477	blk.39.attn_v.weight	0x2a6ab08c0	0x90000
478	blk.39.ffn_down_exps.weight	0x2a6b408c0	0x6c00000
479	blk.39.ffn_gate_exps.weight	0x2ad7408c0	0x5280000
480	blk.39.ffn_gate_inp.weight	0x2b29c08c0	0x100000
481	blk.39.ffn_norm.weight	0x2b2ac08c0	0x2000
482	blk.39.ffn_up_exps.weight	0x2b2ac28c0	0x5280000
483	blk.40.attn_k.weight	0x2b7d428c0	0x6e000
484	blk.40.attn_k_norm.weight	0x2b7db08c0	0x200
485	blk.40.attn_norm.weight	0x2b7db0ac0	0x2000
486	blk.40.attn_output.weight	0x2b7db2ac0	0x480000
487	blk.40.attn_q.weight	0x2b8232ac0	0x370000
488	blk.40.attn_q_norm.weight	0x2b85a2ac0	0x200
489	blk.40.attn_v.weight	0x2b85a2cc0	0x90000
490	blk.40.ffn_down_exps.weight	0x2b8632cc0	0x6c00000
491	blk.40.ffn_gate_exps.weight	0x2bf232cc0	0x5280000
492	blk.40.ffn_gate_inp.weight	0x2c44b2cc0	0x100000
493	blk.40.ffn_norm.weight	0x2c45b2cc0	0x2000
494	blk.40.ffn_up_exps.weight	0x2c45b4cc0	0x5280000
495	blk.41.attn_k.weight	0x2c9834cc0	0x6e000
496	blk.41.attn_k_norm.weight	0x2c98a2cc0	0x200
497	blk.41.attn_norm.weight	0x2c98a2ec0	0x2000
498	blk.41.attn_output.weight	0x2c98a4ec0	0x480000
499	blk.41.attn_q.weight	0x2c9d24ec0	0x370000
500	blk.41.attn_q_norm.weight	0x2ca094ec0	0x200
501	blk.41.attn_v.weight	0x2ca0950c0	0x90000
502	blk.41.ffn_down_exps.weight	0x2ca1250c0	0x6c00000
503	blk.41.ffn_gate_exps.weight	0x2d0d250c0	0x5280000
504	blk.41.ffn_gate_inp.weight	0x2d5fa50c0	0x100000
505	blk.41.ffn_norm.weight	0x2d60a50c0	0x2000
506	blk.41.ffn_up_exps.weight	0x2d60a70c0	0x5280000
507	blk.42.attn_k.weight	0x2db3270c0	0x6e000
508	blk.42.attn_k_norm.weight	0x2db3950c0	0x200
509	blk.42.attn_norm.weight	0x2db3952c0	0x2000
510	blk.42.attn_output.weight	0x2db3972c0	0x480000
511	blk.42.attn_q.weight	0x2db8172c0	0x370000
512	blk.42.attn_q_norm.weight	0x2dbb872c0	0x200
513	blk.42.attn_v.weight	0x2dbb874c0	0x90000
514	blk.42.ffn_down_exps.weight	0x2dbc174c0	0x6c00000
515	blk.42.ffn_gate_exps.weight	0x2e28174c0	0x5280000
516	blk.42.ffn_gate_inp.weight	0x2e7a974c0	0x100000
517	blk.42.ffn_norm.weight	0x2e7b974c0	0x2000
518	blk.42.ffn_up_exps.weight	0x2e7b994c0	0x5280000
519	blk.43.attn_k.weight	0x2ece194c0	0x6e000
520	blk.43.attn_k_norm.weight	0x2ece874c0	0x200
521	blk.43.attn_norm.weight	0x2ece876c0	0x2000
522	blk.43.attn_output.weight	0x2ece896c0	0x480000
523	blk.43.attn_q.weight	0x2ed3096c0	0x370000
524	blk.43.attn_q_norm.weight	0x2ed6796c0	0x200
525	blk.43.attn_v.weight	0x2ed6798c0	0x90000
526	blk.43.ffn_down_exps.weight	0x2ed7098c0	0x6c00000
527	blk.43.ffn_gate_exps.weight	0x2f43098c0	0x5280000
528	blk.43.ffn_gate_inp.weight	0x2f95898c0	0x100000
529	blk.43.ffn_norm.weight	0x2f96898c0	0x2000
530	blk.43.ffn_up_exps.weight	0x2f968b8c0	0x5280000
531	blk.44.attn_k.weight	0x2fe90b8c0	0x6e000
532	blk.44.attn_k_norm.weight	0x2fe9798c0	0x200
533	blk.44.attn_norm.weight	0x2fe979ac0	0x2000
534	blk.44.attn_output.weight	0x2fe97bac0	0x480000
535	blk.44.attn_q.weight	0x2fedfbac0	0x370000
536	blk.44.attn_q_norm.weight	0x2ff16bac0	0x200
537	blk.44.attn_v.weight	0x2ff16bcc0	0x90000
538	blk.44.ffn_down_exps.weight	0x2ff1fbcc0	0x6c00000
539	blk.44.ffn_gate_exps.weight	0x305dfbcc0	0x5280000
540	blk.44.ffn_gate_inp.weight	0x30b07bcc0	0x100000
541	blk.44.ffn_norm.weight	0x30b17bcc0	0x2000
542	blk.44.ffn_up_exps.weight	0x30b17dcc0	0x5280000
543	blk.45.attn_k.weight	0x3103fdcc0	0x6e000
544	blk.45.attn_k_norm.weight	0x31046bcc0	0x200
545	blk.45.attn_norm.weight	0x31046bec0	0x2000
546	blk.45.attn_output.weight	0x31046dec0	0x480000
547	blk.45.attn_q.weight	0x3108edec0	0x370000
548	blk.45.attn_q_norm.weight	0x310c5dec0	0x200
549	blk.45.attn_v.weight	0x310c5e0c0	0x90000
550	blk.45.ffn_down_exps.weight	0x310cee0c0	0x6c00000
551	blk.45.ffn_gate_exps.weight	0x3178ee0c0	0x5280000
552	blk.45.ffn_gate_inp.weight	0x31cb6e0c0	0x100000
553	blk.45.ffn_norm.weight	0x31cc6e0c0	0x2000
554	blk.45.ffn_up_exps.weight	0x31cc700c0	0x5280000
555	blk.46.attn_k.weight	0x321ef00c0	0x6e000
556	blk.46.attn_k_norm.weight	0x321f5e0c0	0x200
557	blk.46.attn_norm.weight	0x321f5e2c0	0x2000
558	blk.46.attn_output.weight	0x321f602c0	0x480000
559	blk.46.attn_q.weight	0x3223e02c0	0x370000
560	blk.46.attn_q_norm.weight	0x3227502c0	0x200
561	blk.46.attn_v.weight	0x3227504c0	0x90000
562	blk.46.ffn_down_exps.weight	0x3227e04c0	0x6c00000
563	blk.46.ffn_gate_exps.weight	0x3293e04c0	0x5280000
564	blk.46.ffn_gate_inp.weight	0x32e6604c0	0x100000
565	blk.46.ffn_norm.weight	0x32e7604c0	0x2000
566	blk.46.ffn_up_exps.weight	0x32e7624c0	0x5280000
567	blk.47.attn_k.weight	0x3339e24c0	0x6e000
568	blk.47.attn_k_norm.weight	0x333a504c0	0x200
569	blk.47.attn_norm.weight	0x333a506c0	0x2000
570	blk.47.attn_output.weight	0x333a526c0	0x480000
571	blk.47.attn_q.weight	0x333ed26c0	0x370000
572	blk.47.attn_q_norm.weight	0x3342426c0	0x200
573	blk.47.attn_v.weight	0x3342428c0	0x90000
574	blk.47.ffn_down_exps.weight	0x3342d28c0	0x6c00000
575	blk.47.ffn_gate_exps.weight	0x33aed28c0	0x5280000
576	blk.47.ffn_gate_inp.weight	0x3401528c0	0x100000
577	blk.47.ffn_norm.weight	0x3402528c0	0x2000
578	blk.47.ffn_up_exps.weight	0x3402548c0	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	IQ3_S
1	output_norm.weight	Output Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
2	token_embd.weight	Token Embedding (W)	(~311M) 311164928	2048 x 151936 x 1 x 1	IQ3_S

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

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	IQ3_XXS
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	Q4_K
7	blk.0.attn_q.weight	Block 0 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
10	blk.0.ffn_down_exps.weight	Block 0 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
11	blk.0.ffn_gate_exps.weight	Block 0 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
12	blk.0.ffn_gate_inp.weight	Block 0 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
13	blk.0.ffn_norm.weight	Block 0 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
14	blk.0.ffn_up_exps.weight	Block 0 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
19	blk.1.attn_q.weight	Block 1 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
22	blk.1.ffn_down_exps.weight	Block 1 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
23	blk.1.ffn_gate_exps.weight	Block 1 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
24	blk.1.ffn_gate_inp.weight	Block 1 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
25	blk.1.ffn_norm.weight	Block 1 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
26	blk.1.ffn_up_exps.weight	Block 1 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
31	blk.2.attn_q.weight	Block 2 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
34	blk.2.ffn_down_exps.weight	Block 2 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
35	blk.2.ffn_gate_exps.weight	Block 2 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
36	blk.2.ffn_gate_inp.weight	Block 2 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
37	blk.2.ffn_norm.weight	Block 2 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
38	blk.2.ffn_up_exps.weight	Block 2 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
43	blk.3.attn_q.weight	Block 3 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
46	blk.3.ffn_down_exps.weight	Block 3 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
47	blk.3.ffn_gate_exps.weight	Block 3 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
48	blk.3.ffn_gate_inp.weight	Block 3 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
49	blk.3.ffn_norm.weight	Block 3 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
50	blk.3.ffn_up_exps.weight	Block 3 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
55	blk.4.attn_q.weight	Block 4 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
58	blk.4.ffn_down_exps.weight	Block 4 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
59	blk.4.ffn_gate_exps.weight	Block 4 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
60	blk.4.ffn_gate_inp.weight	Block 4 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
61	blk.4.ffn_norm.weight	Block 4 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
62	blk.4.ffn_up_exps.weight	Block 4 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
67	blk.5.attn_q.weight	Block 5 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
70	blk.5.ffn_down_exps.weight	Block 5 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
71	blk.5.ffn_gate_exps.weight	Block 5 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
72	blk.5.ffn_gate_inp.weight	Block 5 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
73	blk.5.ffn_norm.weight	Block 5 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
74	blk.5.ffn_up_exps.weight	Block 5 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
79	blk.6.attn_q.weight	Block 6 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
82	blk.6.ffn_down_exps.weight	Block 6 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
83	blk.6.ffn_gate_exps.weight	Block 6 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
84	blk.6.ffn_gate_inp.weight	Block 6 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
85	blk.6.ffn_norm.weight	Block 6 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
86	blk.6.ffn_up_exps.weight	Block 6 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
91	blk.7.attn_q.weight	Block 7 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
94	blk.7.ffn_down_exps.weight	Block 7 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
95	blk.7.ffn_gate_exps.weight	Block 7 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
96	blk.7.ffn_gate_inp.weight	Block 7 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
97	blk.7.ffn_norm.weight	Block 7 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
98	blk.7.ffn_up_exps.weight	Block 7 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
103	blk.8.attn_q.weight	Block 8 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
106	blk.8.ffn_down_exps.weight	Block 8 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
107	blk.8.ffn_gate_exps.weight	Block 8 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
108	blk.8.ffn_gate_inp.weight	Block 8 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
109	blk.8.ffn_norm.weight	Block 8 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
110	blk.8.ffn_up_exps.weight	Block 8 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
115	blk.9.attn_q.weight	Block 9 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
118	blk.9.ffn_down_exps.weight	Block 9 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
119	blk.9.ffn_gate_exps.weight	Block 9 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
120	blk.9.ffn_gate_inp.weight	Block 9 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
121	blk.9.ffn_norm.weight	Block 9 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
122	blk.9.ffn_up_exps.weight	Block 9 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
127	blk.10.attn_q.weight	Block 10 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
130	blk.10.ffn_down_exps.weight	Block 10 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
131	blk.10.ffn_gate_exps.weight	Block 10 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
132	blk.10.ffn_gate_inp.weight	Block 10 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
133	blk.10.ffn_norm.weight	Block 10 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
134	blk.10.ffn_up_exps.weight	Block 10 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
139	blk.11.attn_q.weight	Block 11 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
142	blk.11.ffn_down_exps.weight	Block 11 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
143	blk.11.ffn_gate_exps.weight	Block 11 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
144	blk.11.ffn_gate_inp.weight	Block 11 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
145	blk.11.ffn_norm.weight	Block 11 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
146	blk.11.ffn_up_exps.weight	Block 11 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
151	blk.12.attn_q.weight	Block 12 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
154	blk.12.ffn_down_exps.weight	Block 12 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
155	blk.12.ffn_gate_exps.weight	Block 12 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
156	blk.12.ffn_gate_inp.weight	Block 12 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
157	blk.12.ffn_norm.weight	Block 12 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
158	blk.12.ffn_up_exps.weight	Block 12 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
163	blk.13.attn_q.weight	Block 13 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
166	blk.13.ffn_down_exps.weight	Block 13 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
167	blk.13.ffn_gate_exps.weight	Block 13 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_XXS
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	Q4_K
175	blk.14.attn_q.weight	Block 14 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
178	blk.14.ffn_down_exps.weight	Block 14 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
179	blk.14.ffn_gate_exps.weight	Block 14 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
180	blk.14.ffn_gate_inp.weight	Block 14 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
181	blk.14.ffn_norm.weight	Block 14 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
182	blk.14.ffn_up_exps.weight	Block 14 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
187	blk.15.attn_q.weight	Block 15 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
190	blk.15.ffn_down_exps.weight	Block 15 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
191	blk.15.ffn_gate_exps.weight	Block 15 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_XXS
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	Q4_K
199	blk.16.attn_q.weight	Block 16 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
202	blk.16.ffn_down_exps.weight	Block 16 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
203	blk.16.ffn_gate_exps.weight	Block 16 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
204	blk.16.ffn_gate_inp.weight	Block 16 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
205	blk.16.ffn_norm.weight	Block 16 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
206	blk.16.ffn_up_exps.weight	Block 16 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
211	blk.17.attn_q.weight	Block 17 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
214	blk.17.ffn_down_exps.weight	Block 17 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
215	blk.17.ffn_gate_exps.weight	Block 17 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
216	blk.17.ffn_gate_inp.weight	Block 17 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
217	blk.17.ffn_norm.weight	Block 17 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
218	blk.17.ffn_up_exps.weight	Block 17 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
223	blk.18.attn_q.weight	Block 18 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
226	blk.18.ffn_down_exps.weight	Block 18 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
227	blk.18.ffn_gate_exps.weight	Block 18 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
228	blk.18.ffn_gate_inp.weight	Block 18 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
229	blk.18.ffn_norm.weight	Block 18 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
230	blk.18.ffn_up_exps.weight	Block 18 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
235	blk.19.attn_q.weight	Block 19 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
238	blk.19.ffn_down_exps.weight	Block 19 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
239	blk.19.ffn_gate_exps.weight	Block 19 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
240	blk.19.ffn_gate_inp.weight	Block 19 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
241	blk.19.ffn_norm.weight	Block 19 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
242	blk.19.ffn_up_exps.weight	Block 19 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
247	blk.20.attn_q.weight	Block 20 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
250	blk.20.ffn_down_exps.weight	Block 20 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
251	blk.20.ffn_gate_exps.weight	Block 20 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
252	blk.20.ffn_gate_inp.weight	Block 20 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
253	blk.20.ffn_norm.weight	Block 20 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
254	blk.20.ffn_up_exps.weight	Block 20 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
259	blk.21.attn_q.weight	Block 21 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
262	blk.21.ffn_down_exps.weight	Block 21 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
263	blk.21.ffn_gate_exps.weight	Block 21 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
264	blk.21.ffn_gate_inp.weight	Block 21 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
265	blk.21.ffn_norm.weight	Block 21 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
266	blk.21.ffn_up_exps.weight	Block 21 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
271	blk.22.attn_q.weight	Block 22 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
274	blk.22.ffn_down_exps.weight	Block 22 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
275	blk.22.ffn_gate_exps.weight	Block 22 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
276	blk.22.ffn_gate_inp.weight	Block 22 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
277	blk.22.ffn_norm.weight	Block 22 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
278	blk.22.ffn_up_exps.weight	Block 22 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_XXS
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	Q4_K
283	blk.23.attn_q.weight	Block 23 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_XXS
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	IQ3_S
286	blk.23.ffn_down_exps.weight	Block 23 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
287	blk.23.ffn_gate_exps.weight	Block 23 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
288	blk.23.ffn_gate_inp.weight	Block 23 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
289	blk.23.ffn_norm.weight	Block 23 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
290	blk.23.ffn_up_exps.weight	Block 23 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_S
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	Q4_K
295	blk.24.attn_q.weight	Block 24 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
296	blk.24.attn_q_norm.weight	Block 24 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
297	blk.24.attn_v.weight	Block 24 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
298	blk.24.ffn_down_exps.weight	Block 24 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
299	blk.24.ffn_gate_exps.weight	Block 24 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
300	blk.24.ffn_gate_inp.weight	Block 24 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
301	blk.24.ffn_norm.weight	Block 24 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
302	blk.24.ffn_up_exps.weight	Block 24 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_S
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	Q4_K
307	blk.25.attn_q.weight	Block 25 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
308	blk.25.attn_q_norm.weight	Block 25 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
309	blk.25.attn_v.weight	Block 25 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
310	blk.25.ffn_down_exps.weight	Block 25 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
311	blk.25.ffn_gate_exps.weight	Block 25 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
319	blk.26.attn_q.weight	Block 26 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
320	blk.26.attn_q_norm.weight	Block 26 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
321	blk.26.attn_v.weight	Block 26 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
322	blk.26.ffn_down_exps.weight	Block 26 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
323	blk.26.ffn_gate_exps.weight	Block 26 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS
324	blk.26.ffn_gate_inp.weight	Block 26 Expert-Routing Layer For The Feed-Forward Network In Mixture Of Expert Models (W)	(~262K) 262144	2048 x 128 x 1 x 1	F32
325	blk.26.ffn_norm.weight	Block 26 Feed-Forward Network Normalization (W)	( ~2K) 2048	2048 x 1 x 1 x 1	F32
326	blk.26.ffn_up_exps.weight	Block 26 Ffn_Up_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_XXS

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	IQ3_S
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	Q4_K
331	blk.27.attn_q.weight	Block 27 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
332	blk.27.attn_q_norm.weight	Block 27 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
333	blk.27.attn_v.weight	Block 27 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
334	blk.27.ffn_down_exps.weight	Block 27 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
335	blk.27.ffn_gate_exps.weight	Block 27 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
343	blk.28.attn_q.weight	Block 28 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
344	blk.28.attn_q_norm.weight	Block 28 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
345	blk.28.attn_v.weight	Block 28 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
346	blk.28.ffn_down_exps.weight	Block 28 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
347	blk.28.ffn_gate_exps.weight	Block 28 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
355	blk.29.attn_q.weight	Block 29 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
356	blk.29.attn_q_norm.weight	Block 29 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
357	blk.29.attn_v.weight	Block 29 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
358	blk.29.ffn_down_exps.weight	Block 29 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
359	blk.29.ffn_gate_exps.weight	Block 29 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
367	blk.30.attn_q.weight	Block 30 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
368	blk.30.attn_q_norm.weight	Block 30 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
369	blk.30.attn_v.weight	Block 30 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
370	blk.30.ffn_down_exps.weight	Block 30 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
371	blk.30.ffn_gate_exps.weight	Block 30 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
379	blk.31.attn_q.weight	Block 31 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
380	blk.31.attn_q_norm.weight	Block 31 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
381	blk.31.attn_v.weight	Block 31 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
382	blk.31.ffn_down_exps.weight	Block 31 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
383	blk.31.ffn_gate_exps.weight	Block 31 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
391	blk.32.attn_q.weight	Block 32 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
392	blk.32.attn_q_norm.weight	Block 32 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
393	blk.32.attn_v.weight	Block 32 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
394	blk.32.ffn_down_exps.weight	Block 32 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
395	blk.32.ffn_gate_exps.weight	Block 32 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
403	blk.33.attn_q.weight	Block 33 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
404	blk.33.attn_q_norm.weight	Block 33 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
405	blk.33.attn_v.weight	Block 33 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
406	blk.33.ffn_down_exps.weight	Block 33 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
407	blk.33.ffn_gate_exps.weight	Block 33 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
415	blk.34.attn_q.weight	Block 34 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
416	blk.34.attn_q_norm.weight	Block 34 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
417	blk.34.attn_v.weight	Block 34 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
418	blk.34.ffn_down_exps.weight	Block 34 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
419	blk.34.ffn_gate_exps.weight	Block 34 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
427	blk.35.attn_q.weight	Block 35 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
428	blk.35.attn_q_norm.weight	Block 35 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
429	blk.35.attn_v.weight	Block 35 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
430	blk.35.ffn_down_exps.weight	Block 35 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
431	blk.35.ffn_gate_exps.weight	Block 35 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
439	blk.36.attn_q.weight	Block 36 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
440	blk.36.attn_q_norm.weight	Block 36 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
441	blk.36.attn_v.weight	Block 36 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
442	blk.36.ffn_down_exps.weight	Block 36 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
443	blk.36.ffn_gate_exps.weight	Block 36 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
451	blk.37.attn_q.weight	Block 37 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
452	blk.37.attn_q_norm.weight	Block 37 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
453	blk.37.attn_v.weight	Block 37 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
454	blk.37.ffn_down_exps.weight	Block 37 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
455	blk.37.ffn_gate_exps.weight	Block 37 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
463	blk.38.attn_q.weight	Block 38 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
464	blk.38.attn_q_norm.weight	Block 38 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
465	blk.38.attn_v.weight	Block 38 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
466	blk.38.ffn_down_exps.weight	Block 38 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
467	blk.38.ffn_gate_exps.weight	Block 38 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
475	blk.39.attn_q.weight	Block 39 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
476	blk.39.attn_q_norm.weight	Block 39 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
477	blk.39.attn_v.weight	Block 39 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
478	blk.39.ffn_down_exps.weight	Block 39 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
479	blk.39.ffn_gate_exps.weight	Block 39 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
487	blk.40.attn_q.weight	Block 40 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
488	blk.40.attn_q_norm.weight	Block 40 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
489	blk.40.attn_v.weight	Block 40 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
490	blk.40.ffn_down_exps.weight	Block 40 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
491	blk.40.ffn_gate_exps.weight	Block 40 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
499	blk.41.attn_q.weight	Block 41 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
500	blk.41.attn_q_norm.weight	Block 41 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
501	blk.41.attn_v.weight	Block 41 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
502	blk.41.ffn_down_exps.weight	Block 41 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
503	blk.41.ffn_gate_exps.weight	Block 41 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
511	blk.42.attn_q.weight	Block 42 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
512	blk.42.attn_q_norm.weight	Block 42 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
513	blk.42.attn_v.weight	Block 42 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
514	blk.42.ffn_down_exps.weight	Block 42 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
515	blk.42.ffn_gate_exps.weight	Block 42 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
523	blk.43.attn_q.weight	Block 43 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
524	blk.43.attn_q_norm.weight	Block 43 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
525	blk.43.attn_v.weight	Block 43 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
526	blk.43.ffn_down_exps.weight	Block 43 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
527	blk.43.ffn_gate_exps.weight	Block 43 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
535	blk.44.attn_q.weight	Block 44 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
536	blk.44.attn_q_norm.weight	Block 44 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
537	blk.44.attn_v.weight	Block 44 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
538	blk.44.ffn_down_exps.weight	Block 44 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
539	blk.44.ffn_gate_exps.weight	Block 44 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
547	blk.45.attn_q.weight	Block 45 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
548	blk.45.attn_q_norm.weight	Block 45 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
549	blk.45.attn_v.weight	Block 45 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
550	blk.45.ffn_down_exps.weight	Block 45 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
551	blk.45.ffn_gate_exps.weight	Block 45 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
559	blk.46.attn_q.weight	Block 46 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
560	blk.46.attn_q_norm.weight	Block 46 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
561	blk.46.attn_v.weight	Block 46 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
562	blk.46.ffn_down_exps.weight	Block 46 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
563	blk.46.ffn_gate_exps.weight	Block 46 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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	IQ3_S
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	Q4_K
571	blk.47.attn_q.weight	Block 47 Attention Query (W)	( ~8M) 8388608	2048 x 4096 x 1 x 1	IQ3_S
572	blk.47.attn_q_norm.weight	Block 47 Attn_Q_Norm (W)	( 128) 128	128 x 1 x 1 x 1	F32
573	blk.47.attn_v.weight	Block 47 Attention Value (W)	( ~1M) 1048576	2048 x 512 x 1 x 1	IQ4_NL
574	blk.47.ffn_down_exps.weight	Block 47 Ffn_Down_Exps (W)	(~201M) 201326592	768 x 2048 x 128 x 1	IQ4_NL
575	blk.47.ffn_gate_exps.weight	Block 47 Ffn_Gate_Exps (W)	(~201M) 201326592	2048 x 768 x 128 x 1	IQ3_S
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	IQ3_S

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