Instructions to use eaddario/gemma-4-E4B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use eaddario/gemma-4-E4B-it-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf eaddario/gemma-4-E4B-it-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf eaddario/gemma-4-E4B-it-GGUF:F16

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/gemma-4-E4B-it-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf eaddario/gemma-4-E4B-it-GGUF:F16

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/gemma-4-E4B-it-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eaddario/gemma-4-E4B-it-GGUF:F16

Use Docker

docker model run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16

LM Studio
Jan
Ollama
How to use eaddario/gemma-4-E4B-it-GGUF with Ollama:
```
ollama run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16
```
Unsloth Desktop

How to use eaddario/gemma-4-E4B-it-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/gemma-4-E4B-it-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use eaddario/gemma-4-E4B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16
```

Lemonade

How to use eaddario/gemma-4-E4B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eaddario/gemma-4-E4B-it-GGUF:F16

Run and chat with the model

lemonade run user.gemma-4-E4B-it-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use eaddario/gemma-4-E4B-it-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16

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/gemma-4-E4B-it-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use eaddario/gemma-4-E4B-it-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "eaddario/gemma-4-E4B-it-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

gemma-4-E4B-it-GGUF / scores /gemma-4-E4B-it-Q3_K.md

eaddario

Add GGUF internal file structure

8399b2d verified 4 months ago

preview code

Raw

History Blame Contribute Delete

207 kB

gemma-4-E4B-it-Q3_K.gguf - GGUF Internal File Dump

Endian: LITTLE endian

Key Value Metadata Store

There are 52 key-value pairs in this file

POS	TYPE	Count	Key	Value
1	UINT32	1	GGUF.version	3
2	UINT64	1	GGUF.tensor_count	720
3	UINT64	1	GGUF.kv_count	49
4	STRING	1	general.architecture	`gemma4`
5	STRING	1	general.type	`model`
6	INT32	1	general.sampling.top_k	64
7	FLOAT32	1	general.sampling.top_p	0.95
8	FLOAT32	1	general.sampling.temp	1.0
9	STRING	1	general.name	`Gemma 4 E4B It`
10	STRING	1	general.size_label	`7.5B`
11	STRING	1	general.license	`apache-2.0`
12	STRING	1	general.license.link	`https://ai.google.dev/gemma/docs/gemma_4_license`
13	[STRING]	1	general.tags	[ `any-to-any` ]
14	UINT32	1	gemma4.block_count	42
15	UINT32	1	gemma4.context_length	131072
16	UINT32	1	gemma4.embedding_length	2560
17	UINT32	1	gemma4.feed_forward_length	10240
18	UINT32	1	gemma4.attention.head_count	8
19	UINT32	1	gemma4.attention.head_count_kv	2
20	FLOAT32	1	gemma4.rope.freq_base	1e+06
21	FLOAT32	1	gemma4.rope.freq_base_swa	10000.0
22	FLOAT32	1	gemma4.attention.layer_norm_rms_epsilon	1e-06
23	UINT32	1	gemma4.attention.key_length	512
24	UINT32	1	gemma4.attention.value_length	512
25	FLOAT32	1	gemma4.final_logit_softcapping	30.0
26	UINT32	1	gemma4.attention.sliding_window	512
27	UINT32	1	gemma4.attention.shared_kv_layers	18
28	UINT32	1	gemma4.embedding_length_per_layer_input	256
29	[BOOL]	42	gemma4.attention.sliding_window_pattern	[ True, True, True, True, True, False, True, ... ]
30	UINT32	1	gemma4.attention.key_length_swa	256
31	UINT32	1	gemma4.attention.value_length_swa	256
32	UINT32	1	gemma4.rope.dimension_count	512
33	UINT32	1	gemma4.rope.dimension_count_swa	256
34	STRING	1	tokenizer.ggml.model	`gemma4`
35	[STRING]	262144	tokenizer.ggml.tokens	[ `<pad>`, `<eos>`, `<bos>`, `<unk>`, `<mask>`, ... ]
36	[FLOAT32]	262144	tokenizer.ggml.scores	[ -1000.0, -1000.0, -1000.0, -1000.0, -1000.0, -1000.0, -1000.0, ... ]
37	[INT32]	262144	tokenizer.ggml.token_type	[ 3, 3, 3, 3, 3, 1, 1, ... ]
38	[STRING]	514906	tokenizer.ggml.merges	[ `...`, `▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`...`▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁`, `...`, `...`, `▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁`...`▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁`, ... ]
39	UINT32	1	tokenizer.ggml.bos_token_id	2
40	UINT32	1	tokenizer.ggml.eos_token_id	1
41	UINT32	1	tokenizer.ggml.unknown_token_id	3
42	UINT32	1	tokenizer.ggml.padding_token_id	0
43	UINT32	1	tokenizer.ggml.mask_token_id	4
44	STRING	1	tokenizer.chat_template	`{%- macro format_parameters(pr`... `{%- endif -%} {%- endif -%}`
45	BOOL	1	tokenizer.ggml.add_space_prefix	False
46	BOOL	1	tokenizer.ggml.add_bos_token	True
47	UINT32	1	general.quantization_version	2
48	UINT32	1	general.file_type	30
49	STRING	1	quantize.imatrix.file	`gemma-4-E4B-it-WIP/imatrix/ima`...`rix-gemma-4-E4B-it-medium.gguf`
50	STRING	1	quantize.imatrix.dataset	`../datasets/imatrix/combined_eur_medium.txt`
51	UINT32	1	quantize.imatrix.entries_count	342
52	UINT32	1	quantize.imatrix.chunks_count	2471

Tensors Overview ~8B Elements

Total number of elements in all tensors: 7518069290 Elements

/Users/ed/Development/AI/hf/gemma-4-E4B-it-WIP/gemma-4-E4B-it-Q3_K.gguf - GGUF Internal File Dump
- Key Value Metadata Store
- Tensors Overview ~8B 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_norm.weight	0xf173e0	0x2800
1	per_layer_model_proj.weight	0xf19be0	0x3480000
2	per_layer_proj_norm.weight	0x4399be0	0x400
3	per_layer_token_embd.weight	0x4399fe0	0x48300000
4	rope_freqs.weight	0x4c699fe0	0x400
5	token_embd.weight	0x4c69a3e0	0x11300000
6	blk.0.attn_k.weight	0x5d99a3e0	0x89800
7	blk.0.attn_k_norm.weight	0x5da23be0	0x400
8	blk.0.attn_norm.weight	0x5da23fe0	0x2800
9	blk.0.attn_output.weight	0x5da267e0	0x2a8000
10	blk.0.attn_q.weight	0x5dcce7e0	0x226000
11	blk.0.attn_q_norm.weight	0x5def47e0	0x400
12	blk.0.attn_v.weight	0x5def4be0	0xaa000
13	blk.0.ffn_down.weight	0x5df9ebe0	0x834000
14	blk.0.ffn_gate.weight	0x5e7d2be0	0x992000
15	blk.0.ffn_norm.weight	0x5f164be0	0x2800
16	blk.0.ffn_up.weight	0x5f1673e0	0xabe000
17	blk.0.inp_gate.weight	0x5fc253e0	0x34800
18	blk.0.layer_output_scale.weight	0x5fc59be0	0x4
19	blk.0.post_attention_norm.weight	0x5fc59c00	0x2800
20	blk.0.post_ffw_norm.weight	0x5fc5c400	0x2800
21	blk.0.post_norm.weight	0x5fc5ec00	0x2800
22	blk.0.proj.weight	0x5fc61400	0x6e000
23	blk.1.attn_k.weight	0x5fccf400	0x89800
24	blk.1.attn_k_norm.weight	0x5fd58c00	0x400
25	blk.1.attn_norm.weight	0x5fd59000	0x2800
26	blk.1.attn_output.weight	0x5fd5b800	0x2a8000
27	blk.1.attn_q.weight	0x60003800	0x226000
28	blk.1.attn_q_norm.weight	0x60229800	0x400
29	blk.1.attn_v.weight	0x60229c00	0xaa000
30	blk.1.ffn_down.weight	0x602d3c00	0x992000
31	blk.1.ffn_gate.weight	0x60c65c00	0x834000
32	blk.1.ffn_norm.weight	0x61499c00	0x2800
33	blk.1.ffn_up.weight	0x6149c400	0xd48000
34	blk.1.inp_gate.weight	0x621e4400	0x34800
35	blk.1.layer_output_scale.weight	0x62218c00	0x4
36	blk.1.post_attention_norm.weight	0x62218c20	0x2800
37	blk.1.post_ffw_norm.weight	0x6221b420	0x2800
38	blk.1.post_norm.weight	0x6221dc20	0x2800
39	blk.1.proj.weight	0x62220420	0x5a000
40	blk.2.attn_k.weight	0x6227a420	0x89800
41	blk.2.attn_k_norm.weight	0x62303c20	0x400
42	blk.2.attn_norm.weight	0x62304020	0x2800
43	blk.2.attn_output.weight	0x62306820	0x2a8000
44	blk.2.attn_q.weight	0x625ae820	0x226000
45	blk.2.attn_q_norm.weight	0x627d4820	0x400
46	blk.2.attn_v.weight	0x627d4c20	0xaa000
47	blk.2.ffn_down.weight	0x6287ec20	0x992000
48	blk.2.ffn_gate.weight	0x63210c20	0x992000
49	blk.2.ffn_norm.weight	0x63ba2c20	0x2800
50	blk.2.ffn_up.weight	0x63ba5420	0xabe000
51	blk.2.inp_gate.weight	0x64663420	0x5a000
52	blk.2.layer_output_scale.weight	0x646bd420	0x4
53	blk.2.post_attention_norm.weight	0x646bd440	0x2800
54	blk.2.post_ffw_norm.weight	0x646bfc40	0x2800
55	blk.2.post_norm.weight	0x646c2440	0x2800
56	blk.2.proj.weight	0x646c4c40	0x5a000
57	blk.3.attn_k.weight	0x6471ec40	0x89800
58	blk.3.attn_k_norm.weight	0x647a8440	0x400
59	blk.3.attn_norm.weight	0x647a8840	0x2800
60	blk.3.attn_output.weight	0x647ab040	0x2a8000
61	blk.3.attn_q.weight	0x64a53040	0x226000
62	blk.3.attn_q_norm.weight	0x64c79040	0x400
63	blk.3.attn_v.weight	0x64c79440	0xaa000
64	blk.3.ffn_down.weight	0x64d23440	0x992000
65	blk.3.ffn_gate.weight	0x656b5440	0xabe000
66	blk.3.ffn_norm.weight	0x66173440	0x2800
67	blk.3.ffn_up.weight	0x66175c40	0xabe000
68	blk.3.inp_gate.weight	0x66c33c40	0x3d400
69	blk.3.layer_output_scale.weight	0x66c71040	0x4
70	blk.3.post_attention_norm.weight	0x66c71060	0x2800
71	blk.3.post_ffw_norm.weight	0x66c73860	0x2800
72	blk.3.post_norm.weight	0x66c76060	0x2800
73	blk.3.proj.weight	0x66c78860	0x5a000
74	blk.4.attn_k.weight	0x66cd2860	0x89800
75	blk.4.attn_k_norm.weight	0x66d5c060	0x400
76	blk.4.attn_norm.weight	0x66d5c460	0x2800
77	blk.4.attn_output.weight	0x66d5ec60	0x2a8000
78	blk.4.attn_q.weight	0x67006c60	0x226000
79	blk.4.attn_q_norm.weight	0x6722cc60	0x400
80	blk.4.attn_v.weight	0x6722d060	0xaa000
81	blk.4.ffn_down.weight	0x672d7060	0x992000
82	blk.4.ffn_gate.weight	0x67c69060	0x834000
83	blk.4.ffn_norm.weight	0x6849d060	0x2800
84	blk.4.ffn_up.weight	0x6849f860	0xabe000
85	blk.4.inp_gate.weight	0x68f5d860	0x3d400
86	blk.4.layer_output_scale.weight	0x68f9ac60	0x4
87	blk.4.post_attention_norm.weight	0x68f9ac80	0x2800
88	blk.4.post_ffw_norm.weight	0x68f9d480	0x2800
89	blk.4.post_norm.weight	0x68f9fc80	0x2800
90	blk.4.proj.weight	0x68fa2480	0x5a000
91	blk.5.attn_k.weight	0x68ffc480	0x113000
92	blk.5.attn_k_norm.weight	0x6910f480	0x800
93	blk.5.attn_norm.weight	0x6910fc80	0x2800
94	blk.5.attn_output.weight	0x69112480	0x44c000
95	blk.5.attn_q.weight	0x6955e480	0x44c000
96	blk.5.attn_q_norm.weight	0x699aa480	0x800
97	blk.5.attn_v.weight	0x699aac80	0x154000
98	blk.5.ffn_down.weight	0x69afec80	0x992000
99	blk.5.ffn_gate.weight	0x6a490c80	0x834000
100	blk.5.ffn_norm.weight	0x6acc4c80	0x2800
101	blk.5.ffn_up.weight	0x6acc7480	0xd48000
102	blk.5.inp_gate.weight	0x6ba0f480	0x55000
103	blk.5.layer_output_scale.weight	0x6ba64480	0x4
104	blk.5.post_attention_norm.weight	0x6ba644a0	0x2800
105	blk.5.post_ffw_norm.weight	0x6ba66ca0	0x2800
106	blk.5.post_norm.weight	0x6ba694a0	0x2800
107	blk.5.proj.weight	0x6ba6bca0	0x5a000
108	blk.6.attn_k.weight	0x6bac5ca0	0x89800
109	blk.6.attn_k_norm.weight	0x6bb4f4a0	0x400
110	blk.6.attn_norm.weight	0x6bb4f8a0	0x2800
111	blk.6.attn_output.weight	0x6bb520a0	0x2a8000
112	blk.6.attn_q.weight	0x6bdfa0a0	0x226000
113	blk.6.attn_q_norm.weight	0x6c0200a0	0x400
114	blk.6.attn_v.weight	0x6c0204a0	0xaa000
115	blk.6.ffn_down.weight	0x6c0ca4a0	0x992000
116	blk.6.ffn_gate.weight	0x6ca5c4a0	0x834000
117	blk.6.ffn_norm.weight	0x6d2904a0	0x2800
118	blk.6.ffn_up.weight	0x6d292ca0	0xd48000
119	blk.6.inp_gate.weight	0x6dfdaca0	0x5a000
120	blk.6.layer_output_scale.weight	0x6e034ca0	0x4
121	blk.6.post_attention_norm.weight	0x6e034cc0	0x2800
122	blk.6.post_ffw_norm.weight	0x6e0374c0	0x2800
123	blk.6.post_norm.weight	0x6e039cc0	0x2800
124	blk.6.proj.weight	0x6e03c4c0	0x5a000
125	blk.7.attn_k.weight	0x6e0964c0	0x89800
126	blk.7.attn_k_norm.weight	0x6e11fcc0	0x400
127	blk.7.attn_norm.weight	0x6e1200c0	0x2800
128	blk.7.attn_output.weight	0x6e1228c0	0x2a8000
129	blk.7.attn_q.weight	0x6e3ca8c0	0x226000
130	blk.7.attn_q_norm.weight	0x6e5f08c0	0x400
131	blk.7.attn_v.weight	0x6e5f0cc0	0x89800
132	blk.7.ffn_down.weight	0x6e67a4c0	0x992000
133	blk.7.ffn_gate.weight	0x6f00c4c0	0xabe000
134	blk.7.ffn_norm.weight	0x6faca4c0	0x2800
135	blk.7.ffn_up.weight	0x6facccc0	0xabe000
136	blk.7.inp_gate.weight	0x7058acc0	0x5a000
137	blk.7.layer_output_scale.weight	0x705e4cc0	0x4
138	blk.7.post_attention_norm.weight	0x705e4ce0	0x2800
139	blk.7.post_ffw_norm.weight	0x705e74e0	0x2800
140	blk.7.post_norm.weight	0x705e9ce0	0x2800
141	blk.7.proj.weight	0x705ec4e0	0x5a000
142	blk.8.attn_k.weight	0x706464e0	0x89800
143	blk.8.attn_k_norm.weight	0x706cfce0	0x400
144	blk.8.attn_norm.weight	0x706d00e0	0x2800
145	blk.8.attn_output.weight	0x706d28e0	0x2a8000
146	blk.8.attn_q.weight	0x7097a8e0	0x226000
147	blk.8.attn_q_norm.weight	0x70ba08e0	0x400
148	blk.8.attn_v.weight	0x70ba0ce0	0x89800
149	blk.8.ffn_down.weight	0x70c2a4e0	0x992000
150	blk.8.ffn_gate.weight	0x715bc4e0	0xabe000
151	blk.8.ffn_norm.weight	0x7207a4e0	0x2800
152	blk.8.ffn_up.weight	0x7207cce0	0xabe000
153	blk.8.inp_gate.weight	0x72b3ace0	0x3d400
154	blk.8.layer_output_scale.weight	0x72b780e0	0x4
155	blk.8.post_attention_norm.weight	0x72b78100	0x2800
156	blk.8.post_ffw_norm.weight	0x72b7a900	0x2800
157	blk.8.post_norm.weight	0x72b7d100	0x2800
158	blk.8.proj.weight	0x72b7f900	0x5a000
159	blk.9.attn_k.weight	0x72bd9900	0x89800
160	blk.9.attn_k_norm.weight	0x72c63100	0x400
161	blk.9.attn_norm.weight	0x72c63500	0x2800
162	blk.9.attn_output.weight	0x72c65d00	0x2a8000
163	blk.9.attn_q.weight	0x72f0dd00	0x226000
164	blk.9.attn_q_norm.weight	0x73133d00	0x400
165	blk.9.attn_v.weight	0x73134100	0xaa000
166	blk.9.ffn_down.weight	0x731de100	0x992000
167	blk.9.ffn_gate.weight	0x73b70100	0xabe000
168	blk.9.ffn_norm.weight	0x7462e100	0x2800
169	blk.9.ffn_up.weight	0x74630900	0xabe000
170	blk.9.inp_gate.weight	0x750ee900	0x34800
171	blk.9.layer_output_scale.weight	0x75123100	0x4
172	blk.9.post_attention_norm.weight	0x75123120	0x2800
173	blk.9.post_ffw_norm.weight	0x75125920	0x2800
174	blk.9.post_norm.weight	0x75128120	0x2800
175	blk.9.proj.weight	0x7512a920	0x5a000
176	blk.10.attn_k.weight	0x75184920	0x89800
177	blk.10.attn_k_norm.weight	0x7520e120	0x400
178	blk.10.attn_norm.weight	0x7520e520	0x2800
179	blk.10.attn_output.weight	0x75210d20	0x2a8000
180	blk.10.attn_q.weight	0x754b8d20	0x226000
181	blk.10.attn_q_norm.weight	0x756ded20	0x400
182	blk.10.attn_v.weight	0x756df120	0xaa000
183	blk.10.ffn_down.weight	0x75789120	0xabe000
184	blk.10.ffn_gate.weight	0x76247120	0xabe000
185	blk.10.ffn_norm.weight	0x76d05120	0x2800
186	blk.10.ffn_up.weight	0x76d07920	0xabe000
187	blk.10.inp_gate.weight	0x777c5920	0x55000
188	blk.10.layer_output_scale.weight	0x7781a920	0x4
189	blk.10.post_attention_norm.weight	0x7781a940	0x2800
190	blk.10.post_ffw_norm.weight	0x7781d140	0x2800
191	blk.10.post_norm.weight	0x7781f940	0x2800
192	blk.10.proj.weight	0x77822140	0x6e000
193	blk.11.attn_k.weight	0x77890140	0x113000
194	blk.11.attn_k_norm.weight	0x779a3140	0x800
195	blk.11.attn_norm.weight	0x779a3940	0x2800
196	blk.11.attn_output.weight	0x779a6140	0x44c000
197	blk.11.attn_q.weight	0x77df2140	0x44c000
198	blk.11.attn_q_norm.weight	0x7823e140	0x800
199	blk.11.attn_v.weight	0x7823e940	0x154000
200	blk.11.ffn_down.weight	0x78392940	0x992000
201	blk.11.ffn_gate.weight	0x78d24940	0xabe000
202	blk.11.ffn_norm.weight	0x797e2940	0x2800
203	blk.11.ffn_up.weight	0x797e5140	0xabe000
204	blk.11.inp_gate.weight	0x7a2a3140	0x3d400
205	blk.11.layer_output_scale.weight	0x7a2e0540	0x4
206	blk.11.post_attention_norm.weight	0x7a2e0560	0x2800
207	blk.11.post_ffw_norm.weight	0x7a2e2d60	0x2800
208	blk.11.post_norm.weight	0x7a2e5560	0x2800
209	blk.11.proj.weight	0x7a2e7d60	0x5a000
210	blk.12.attn_k.weight	0x7a341d60	0x89800
211	blk.12.attn_k_norm.weight	0x7a3cb560	0x400
212	blk.12.attn_norm.weight	0x7a3cb960	0x2800
213	blk.12.attn_output.weight	0x7a3ce160	0x2a8000
214	blk.12.attn_q.weight	0x7a676160	0x226000
215	blk.12.attn_q_norm.weight	0x7a89c160	0x400
216	blk.12.attn_v.weight	0x7a89c560	0xaa000
217	blk.12.ffn_down.weight	0x7a946560	0x992000
218	blk.12.ffn_gate.weight	0x7b2d8560	0xabe000
219	blk.12.ffn_norm.weight	0x7bd96560	0x2800
220	blk.12.ffn_up.weight	0x7bd98d60	0xabe000
221	blk.12.inp_gate.weight	0x7c856d60	0x3d400
222	blk.12.layer_output_scale.weight	0x7c894160	0x4
223	blk.12.post_attention_norm.weight	0x7c894180	0x2800
224	blk.12.post_ffw_norm.weight	0x7c896980	0x2800
225	blk.12.post_norm.weight	0x7c899180	0x2800
226	blk.12.proj.weight	0x7c89b980	0x5a000
227	blk.13.attn_k.weight	0x7c8f5980	0xaa000
228	blk.13.attn_k_norm.weight	0x7c99f980	0x400
229	blk.13.attn_norm.weight	0x7c99fd80	0x2800
230	blk.13.attn_output.weight	0x7c9a2580	0x2a8000
231	blk.13.attn_q.weight	0x7cc4a580	0x2a8000
232	blk.13.attn_q_norm.weight	0x7cef2580	0x400
233	blk.13.attn_v.weight	0x7cef2980	0xaa000
234	blk.13.ffn_down.weight	0x7cf9c980	0x992000
235	blk.13.ffn_gate.weight	0x7d92e980	0xabe000
236	blk.13.ffn_norm.weight	0x7e3ec980	0x2800
237	blk.13.ffn_up.weight	0x7e3ef180	0xabe000
238	blk.13.inp_gate.weight	0x7eead180	0x3d400
239	blk.13.layer_output_scale.weight	0x7eeea580	0x4
240	blk.13.post_attention_norm.weight	0x7eeea5a0	0x2800
241	blk.13.post_ffw_norm.weight	0x7eeecda0	0x2800
242	blk.13.post_norm.weight	0x7eeef5a0	0x2800
243	blk.13.proj.weight	0x7eef1da0	0xaa000
244	blk.14.attn_k.weight	0x7ef9bda0	0x89800
245	blk.14.attn_k_norm.weight	0x7f0255a0	0x400
246	blk.14.attn_norm.weight	0x7f0259a0	0x2800
247	blk.14.attn_output.weight	0x7f0281a0	0x2a8000
248	blk.14.attn_q.weight	0x7f2d01a0	0x226000
249	blk.14.attn_q_norm.weight	0x7f4f61a0	0x400
250	blk.14.attn_v.weight	0x7f4f65a0	0xaa000
251	blk.14.ffn_down.weight	0x7f5a05a0	0x992000
252	blk.14.ffn_gate.weight	0x7ff325a0	0xabe000
253	blk.14.ffn_norm.weight	0x809f05a0	0x2800
254	blk.14.ffn_up.weight	0x809f2da0	0xabe000
255	blk.14.inp_gate.weight	0x814b0da0	0x55000
256	blk.14.layer_output_scale.weight	0x81505da0	0x4
257	blk.14.post_attention_norm.weight	0x81505dc0	0x2800
258	blk.14.post_ffw_norm.weight	0x815085c0	0x2800
259	blk.14.post_norm.weight	0x8150adc0	0x2800
260	blk.14.proj.weight	0x8150d5c0	0x78000
261	blk.15.attn_k.weight	0x815855c0	0x89800
262	blk.15.attn_k_norm.weight	0x8160edc0	0x400
263	blk.15.attn_norm.weight	0x8160f1c0	0x2800
264	blk.15.attn_output.weight	0x816119c0	0x2a8000
265	blk.15.attn_q.weight	0x818b99c0	0x226000
266	blk.15.attn_q_norm.weight	0x81adf9c0	0x400
267	blk.15.attn_v.weight	0x81adfdc0	0xaa000
268	blk.15.ffn_down.weight	0x81b89dc0	0x992000
269	blk.15.ffn_gate.weight	0x8251bdc0	0xabe000
270	blk.15.ffn_norm.weight	0x82fd9dc0	0x2800
271	blk.15.ffn_up.weight	0x82fdc5c0	0xabe000
272	blk.15.inp_gate.weight	0x83a9a5c0	0x3d400
273	blk.15.layer_output_scale.weight	0x83ad79c0	0x4
274	blk.15.post_attention_norm.weight	0x83ad79e0	0x2800
275	blk.15.post_ffw_norm.weight	0x83ada1e0	0x2800
276	blk.15.post_norm.weight	0x83adc9e0	0x2800
277	blk.15.proj.weight	0x83adf1e0	0x6e000
278	blk.16.attn_k.weight	0x83b4d1e0	0x89800
279	blk.16.attn_k_norm.weight	0x83bd69e0	0x400
280	blk.16.attn_norm.weight	0x83bd6de0	0x2800
281	blk.16.attn_output.weight	0x83bd95e0	0x2a8000
282	blk.16.attn_q.weight	0x83e815e0	0x226000
283	blk.16.attn_q_norm.weight	0x840a75e0	0x400
284	blk.16.attn_v.weight	0x840a79e0	0xaa000
285	blk.16.ffn_down.weight	0x841519e0	0xabe000
286	blk.16.ffn_gate.weight	0x84c0f9e0	0xabe000
287	blk.16.ffn_norm.weight	0x856cd9e0	0x2800
288	blk.16.ffn_up.weight	0x856d01e0	0xabe000
289	blk.16.inp_gate.weight	0x8618e1e0	0x3d400
290	blk.16.layer_output_scale.weight	0x861cb5e0	0x4
291	blk.16.post_attention_norm.weight	0x861cb600	0x2800
292	blk.16.post_ffw_norm.weight	0x861cde00	0x2800
293	blk.16.post_norm.weight	0x861d0600	0x2800
294	blk.16.proj.weight	0x861d2e00	0x64000
295	blk.17.attn_k.weight	0x86236e00	0x113000
296	blk.17.attn_k_norm.weight	0x86349e00	0x800
297	blk.17.attn_norm.weight	0x8634a600	0x2800
298	blk.17.attn_output.weight	0x8634ce00	0x44c000
299	blk.17.attn_q.weight	0x86798e00	0x44c000
300	blk.17.attn_q_norm.weight	0x86be4e00	0x800
301	blk.17.attn_v.weight	0x86be5600	0x154000
302	blk.17.ffn_down.weight	0x86d39600	0xabe000
303	blk.17.ffn_gate.weight	0x877f7600	0xabe000
304	blk.17.ffn_norm.weight	0x882b5600	0x2800
305	blk.17.ffn_up.weight	0x882b7e00	0xabe000
306	blk.17.inp_gate.weight	0x88d75e00	0x55000
307	blk.17.layer_output_scale.weight	0x88dcae00	0x4
308	blk.17.post_attention_norm.weight	0x88dcae20	0x2800
309	blk.17.post_ffw_norm.weight	0x88dcd620	0x2800
310	blk.17.post_norm.weight	0x88dcfe20	0x2800
311	blk.17.proj.weight	0x88dd2620	0x64000
312	blk.18.attn_k.weight	0x88e36620	0xaa000
313	blk.18.attn_k_norm.weight	0x88ee0620	0x400
314	blk.18.attn_norm.weight	0x88ee0a20	0x2800
315	blk.18.attn_output.weight	0x88ee3220	0x2a8000
316	blk.18.attn_q.weight	0x8918b220	0x2a8000
317	blk.18.attn_q_norm.weight	0x89433220	0x400
318	blk.18.attn_v.weight	0x89433620	0xaa000
319	blk.18.ffn_down.weight	0x894dd620	0xabe000
320	blk.18.ffn_gate.weight	0x89f9b620	0xabe000
321	blk.18.ffn_norm.weight	0x8aa59620	0x2800
322	blk.18.ffn_up.weight	0x8aa5be20	0xabe000
323	blk.18.inp_gate.weight	0x8b519e20	0x5a000
324	blk.18.layer_output_scale.weight	0x8b573e20	0x4
325	blk.18.post_attention_norm.weight	0x8b573e40	0x2800
326	blk.18.post_ffw_norm.weight	0x8b576640	0x2800
327	blk.18.post_norm.weight	0x8b578e40	0x2800
328	blk.18.proj.weight	0x8b57b640	0x78000
329	blk.19.attn_k.weight	0x8b5f3640	0xaa000
330	blk.19.attn_k_norm.weight	0x8b69d640	0x400
331	blk.19.attn_norm.weight	0x8b69da40	0x2800
332	blk.19.attn_output.weight	0x8b6a0240	0x2a8000
333	blk.19.attn_q.weight	0x8b948240	0x226000
334	blk.19.attn_q_norm.weight	0x8bb6e240	0x400
335	blk.19.attn_v.weight	0x8bb6e640	0xaa000
336	blk.19.ffn_down.weight	0x8bc18640	0xabe000
337	blk.19.ffn_gate.weight	0x8c6d6640	0xabe000
338	blk.19.ffn_norm.weight	0x8d194640	0x2800
339	blk.19.ffn_up.weight	0x8d196e40	0xabe000
340	blk.19.inp_gate.weight	0x8dc54e40	0x55000
341	blk.19.layer_output_scale.weight	0x8dca9e40	0x4
342	blk.19.post_attention_norm.weight	0x8dca9e60	0x2800
343	blk.19.post_ffw_norm.weight	0x8dcac660	0x2800
344	blk.19.post_norm.weight	0x8dcaee60	0x2800
345	blk.19.proj.weight	0x8dcb1660	0x5a000
346	blk.20.attn_k.weight	0x8dd0b660	0xaa000
347	blk.20.attn_k_norm.weight	0x8ddb5660	0x400
348	blk.20.attn_norm.weight	0x8ddb5a60	0x2800
349	blk.20.attn_output.weight	0x8ddb8260	0x2a8000
350	blk.20.attn_q.weight	0x8e060260	0x2a8000
351	blk.20.attn_q_norm.weight	0x8e308260	0x400
352	blk.20.attn_v.weight	0x8e308660	0xaa000
353	blk.20.ffn_down.weight	0x8e3b2660	0xabe000
354	blk.20.ffn_gate.weight	0x8ee70660	0xabe000
355	blk.20.ffn_norm.weight	0x8f92e660	0x2800
356	blk.20.ffn_up.weight	0x8f930e60	0xabe000
357	blk.20.inp_gate.weight	0x903eee60	0x3d400
358	blk.20.layer_output_scale.weight	0x9042c260	0x4
359	blk.20.post_attention_norm.weight	0x9042c280	0x2800
360	blk.20.post_ffw_norm.weight	0x9042ea80	0x2800
361	blk.20.post_norm.weight	0x90431280	0x2800
362	blk.20.proj.weight	0x90433a80	0x5a000
363	blk.21.attn_k.weight	0x9048da80	0xaa000
364	blk.21.attn_k_norm.weight	0x90537a80	0x400
365	blk.21.attn_norm.weight	0x90537e80	0x2800
366	blk.21.attn_output.weight	0x9053a680	0x2a8000
367	blk.21.attn_q.weight	0x907e2680	0x2a8000
368	blk.21.attn_q_norm.weight	0x90a8a680	0x400
369	blk.21.attn_v.weight	0x90a8aa80	0xaa000
370	blk.21.ffn_down.weight	0x90b34a80	0xabe000
371	blk.21.ffn_gate.weight	0x915f2a80	0xabe000
372	blk.21.ffn_norm.weight	0x920b0a80	0x2800
373	blk.21.ffn_up.weight	0x920b3280	0xd48000
374	blk.21.inp_gate.weight	0x92dfb280	0x3d400
375	blk.21.layer_output_scale.weight	0x92e38680	0x4
376	blk.21.post_attention_norm.weight	0x92e386a0	0x2800
377	blk.21.post_ffw_norm.weight	0x92e3aea0	0x2800
378	blk.21.post_norm.weight	0x92e3d6a0	0x2800
379	blk.21.proj.weight	0x92e3fea0	0x6e000
380	blk.22.attn_k.weight	0x92eadea0	0xaa000
381	blk.22.attn_k_norm.weight	0x92f57ea0	0x400
382	blk.22.attn_norm.weight	0x92f582a0	0x2800
383	blk.22.attn_output.weight	0x92f5aaa0	0x2a8000
384	blk.22.attn_q.weight	0x93202aa0	0x2a8000
385	blk.22.attn_q_norm.weight	0x934aaaa0	0x400
386	blk.22.attn_v.weight	0x934aaea0	0xaa000
387	blk.22.ffn_down.weight	0x93554ea0	0x992000
388	blk.22.ffn_gate.weight	0x93ee6ea0	0xabe000
389	blk.22.ffn_norm.weight	0x949a4ea0	0x2800
390	blk.22.ffn_up.weight	0x949a76a0	0xabe000
391	blk.22.inp_gate.weight	0x954656a0	0x34800
392	blk.22.layer_output_scale.weight	0x95499ea0	0x4
393	blk.22.post_attention_norm.weight	0x95499ec0	0x2800
394	blk.22.post_ffw_norm.weight	0x9549c6c0	0x2800
395	blk.22.post_norm.weight	0x9549eec0	0x2800
396	blk.22.proj.weight	0x954a16c0	0x5a000
397	blk.23.attn_k.weight	0x954fb6c0	0x154000
398	blk.23.attn_k_norm.weight	0x9564f6c0	0x800
399	blk.23.attn_norm.weight	0x9564fec0	0x2800
400	blk.23.attn_output.weight	0x956526c0	0x44c000
401	blk.23.attn_q.weight	0x95a9e6c0	0x550000
402	blk.23.attn_q_norm.weight	0x95fee6c0	0x800
403	blk.23.attn_v.weight	0x95feeec0	0x154000
404	blk.23.ffn_down.weight	0x96142ec0	0xabe000
405	blk.23.ffn_gate.weight	0x96c00ec0	0xabe000
406	blk.23.ffn_norm.weight	0x976beec0	0x2800
407	blk.23.ffn_up.weight	0x976c16c0	0xd48000
408	blk.23.inp_gate.weight	0x984096c0	0x55000
409	blk.23.layer_output_scale.weight	0x9845e6c0	0x4
410	blk.23.post_attention_norm.weight	0x9845e6e0	0x2800
411	blk.23.post_ffw_norm.weight	0x98460ee0	0x2800
412	blk.23.post_norm.weight	0x984636e0	0x2800
413	blk.23.proj.weight	0x98465ee0	0x64000
414	blk.24.attn_k.weight	0x984c9ee0	0xb4000
415	blk.24.attn_k_norm.weight	0x9857dee0	0x400
416	blk.24.attn_norm.weight	0x9857e2e0	0x2800
417	blk.24.attn_output.weight	0x98580ae0	0x2a8000
418	blk.24.attn_q.weight	0x98828ae0	0x2a8000
419	blk.24.attn_q_norm.weight	0x98ad0ae0	0x400
420	blk.24.attn_v.weight	0x98ad0ee0	0x89800
421	blk.24.ffn_down.weight	0x98b5a6e0	0xabe000
422	blk.24.ffn_gate.weight	0x996186e0	0xabe000
423	blk.24.ffn_norm.weight	0x9a0d66e0	0x2800
424	blk.24.ffn_up.weight	0x9a0d8ee0	0xd48000
425	blk.24.inp_gate.weight	0x9ae20ee0	0x55000
426	blk.24.layer_output_scale.weight	0x9ae75ee0	0x4
427	blk.24.post_attention_norm.weight	0x9ae75f00	0x2800
428	blk.24.post_ffw_norm.weight	0x9ae78700	0x2800
429	blk.24.post_norm.weight	0x9ae7af00	0x2800
430	blk.24.proj.weight	0x9ae7d700	0x5a000
431	blk.25.attn_k.weight	0x9aed7700	0xb4000
432	blk.25.attn_k_norm.weight	0x9af8b700	0x400
433	blk.25.attn_norm.weight	0x9af8bb00	0x2800
434	blk.25.attn_output.weight	0x9af8e300	0x2a8000
435	blk.25.attn_q.weight	0x9b236300	0x226000
436	blk.25.attn_q_norm.weight	0x9b45c300	0x400
437	blk.25.attn_v.weight	0x9b45c700	0x89800
438	blk.25.ffn_down.weight	0x9b4e5f00	0xabe000
439	blk.25.ffn_gate.weight	0x9bfa3f00	0xabe000
440	blk.25.ffn_norm.weight	0x9ca61f00	0x2800
441	blk.25.ffn_up.weight	0x9ca64700	0xabe000
442	blk.25.inp_gate.weight	0x9d522700	0x55000
443	blk.25.layer_output_scale.weight	0x9d577700	0x4
444	blk.25.post_attention_norm.weight	0x9d577720	0x2800
445	blk.25.post_ffw_norm.weight	0x9d579f20	0x2800
446	blk.25.post_norm.weight	0x9d57c720	0x2800
447	blk.25.proj.weight	0x9d57ef20	0x64000
448	blk.26.attn_k.weight	0x9d5e2f20	0xb4000
449	blk.26.attn_k_norm.weight	0x9d696f20	0x400
450	blk.26.attn_norm.weight	0x9d697320	0x2800
451	blk.26.attn_output.weight	0x9d699b20	0x2a8000
452	blk.26.attn_q.weight	0x9d941b20	0x226000
453	blk.26.attn_q_norm.weight	0x9db67b20	0x400
454	blk.26.attn_v.weight	0x9db67f20	0x89800
455	blk.26.ffn_down.weight	0x9dbf1720	0xabe000
456	blk.26.ffn_gate.weight	0x9e6af720	0xabe000
457	blk.26.ffn_norm.weight	0x9f16d720	0x2800
458	blk.26.ffn_up.weight	0x9f16ff20	0xabe000
459	blk.26.inp_gate.weight	0x9fc2df20	0x34800
460	blk.26.layer_output_scale.weight	0x9fc62720	0x4
461	blk.26.post_attention_norm.weight	0x9fc62740	0x2800
462	blk.26.post_ffw_norm.weight	0x9fc64f40	0x2800
463	blk.26.post_norm.weight	0x9fc67740	0x2800
464	blk.26.proj.weight	0x9fc69f40	0x6e000
465	blk.27.attn_k.weight	0x9fcd7f40	0xb4000
466	blk.27.attn_k_norm.weight	0x9fd8bf40	0x400
467	blk.27.attn_norm.weight	0x9fd8c340	0x2800
468	blk.27.attn_output.weight	0x9fd8eb40	0x2a8000
469	blk.27.attn_q.weight	0xa0036b40	0x226000
470	blk.27.attn_q_norm.weight	0xa025cb40	0x400
471	blk.27.attn_v.weight	0xa025cf40	0x89800
472	blk.27.ffn_down.weight	0xa02e6740	0xabe000
473	blk.27.ffn_gate.weight	0xa0da4740	0xabe000
474	blk.27.ffn_norm.weight	0xa1862740	0x2800
475	blk.27.ffn_up.weight	0xa1864f40	0xabe000
476	blk.27.inp_gate.weight	0xa2322f40	0x55000
477	blk.27.layer_output_scale.weight	0xa2377f40	0x4
478	blk.27.post_attention_norm.weight	0xa2377f60	0x2800
479	blk.27.post_ffw_norm.weight	0xa237a760	0x2800
480	blk.27.post_norm.weight	0xa237cf60	0x2800
481	blk.27.proj.weight	0xa237f760	0x78000
482	blk.28.attn_k.weight	0xa23f7760	0xb4000
483	blk.28.attn_k_norm.weight	0xa24ab760	0x400
484	blk.28.attn_norm.weight	0xa24abb60	0x2800
485	blk.28.attn_output.weight	0xa24ae360	0x2a8000
486	blk.28.attn_q.weight	0xa2756360	0x226000
487	blk.28.attn_q_norm.weight	0xa297c360	0x400
488	blk.28.attn_v.weight	0xa297c760	0x89800
489	blk.28.ffn_down.weight	0xa2a05f60	0xabe000
490	blk.28.ffn_gate.weight	0xa34c3f60	0xabe000
491	blk.28.ffn_norm.weight	0xa3f81f60	0x2800
492	blk.28.ffn_up.weight	0xa3f84760	0xabe000
493	blk.28.inp_gate.weight	0xa4a42760	0x44c00
494	blk.28.layer_output_scale.weight	0xa4a87360	0x4
495	blk.28.post_attention_norm.weight	0xa4a87380	0x2800
496	blk.28.post_ffw_norm.weight	0xa4a89b80	0x2800
497	blk.28.post_norm.weight	0xa4a8c380	0x2800
498	blk.28.proj.weight	0xa4a8eb80	0x78000
499	blk.29.attn_k.weight	0xa4b06b80	0x168000
500	blk.29.attn_k_norm.weight	0xa4c6eb80	0x800
501	blk.29.attn_norm.weight	0xa4c6f380	0x2800
502	blk.29.attn_output.weight	0xa4c71b80	0x44c000
503	blk.29.attn_q.weight	0xa50bdb80	0x44c000
504	blk.29.attn_q_norm.weight	0xa5509b80	0x800
505	blk.29.attn_v.weight	0xa550a380	0x113000
506	blk.29.ffn_down.weight	0xa561d380	0xabe000
507	blk.29.ffn_gate.weight	0xa60db380	0xabe000
508	blk.29.ffn_norm.weight	0xa6b99380	0x2800
509	blk.29.ffn_up.weight	0xa6b9bb80	0xabe000
510	blk.29.inp_gate.weight	0xa7659b80	0x55000
511	blk.29.layer_output_scale.weight	0xa76aeb80	0x4
512	blk.29.post_attention_norm.weight	0xa76aeba0	0x2800
513	blk.29.post_ffw_norm.weight	0xa76b13a0	0x2800
514	blk.29.post_norm.weight	0xa76b3ba0	0x2800
515	blk.29.proj.weight	0xa76b63a0	0x6e000
516	blk.30.attn_k.weight	0xa77243a0	0xb4000
517	blk.30.attn_k_norm.weight	0xa77d83a0	0x400
518	blk.30.attn_norm.weight	0xa77d87a0	0x2800
519	blk.30.attn_output.weight	0xa77dafa0	0x2a8000
520	blk.30.attn_q.weight	0xa7a82fa0	0x2a8000
521	blk.30.attn_q_norm.weight	0xa7d2afa0	0x400
522	blk.30.attn_v.weight	0xa7d2b3a0	0x89800
523	blk.30.ffn_down.weight	0xa7db4ba0	0xabe000
524	blk.30.ffn_gate.weight	0xa8872ba0	0xabe000
525	blk.30.ffn_norm.weight	0xa9330ba0	0x2800
526	blk.30.ffn_up.weight	0xa93333a0	0xabe000
527	blk.30.inp_gate.weight	0xa9df13a0	0x55000
528	blk.30.layer_output_scale.weight	0xa9e463a0	0x4
529	blk.30.post_attention_norm.weight	0xa9e463c0	0x2800
530	blk.30.post_ffw_norm.weight	0xa9e48bc0	0x2800
531	blk.30.post_norm.weight	0xa9e4b3c0	0x2800
532	blk.30.proj.weight	0xa9e4dbc0	0x5a000
533	blk.31.attn_k.weight	0xa9ea7bc0	0xb4000
534	blk.31.attn_k_norm.weight	0xa9f5bbc0	0x400
535	blk.31.attn_norm.weight	0xa9f5bfc0	0x2800
536	blk.31.attn_output.weight	0xa9f5e7c0	0x2a8000
537	blk.31.attn_q.weight	0xaa2067c0	0x226000
538	blk.31.attn_q_norm.weight	0xaa42c7c0	0x400
539	blk.31.attn_v.weight	0xaa42cbc0	0x89800
540	blk.31.ffn_down.weight	0xaa4b63c0	0xabe000
541	blk.31.ffn_gate.weight	0xaaf743c0	0xabe000
542	blk.31.ffn_norm.weight	0xaba323c0	0x2800
543	blk.31.ffn_up.weight	0xaba34bc0	0xabe000
544	blk.31.inp_gate.weight	0xac4f2bc0	0x55000
545	blk.31.layer_output_scale.weight	0xac547bc0	0x4
546	blk.31.post_attention_norm.weight	0xac547be0	0x2800
547	blk.31.post_ffw_norm.weight	0xac54a3e0	0x2800
548	blk.31.post_norm.weight	0xac54cbe0	0x2800
549	blk.31.proj.weight	0xac54f3e0	0x78000
550	blk.32.attn_k.weight	0xac5c73e0	0xb4000
551	blk.32.attn_k_norm.weight	0xac67b3e0	0x400
552	blk.32.attn_norm.weight	0xac67b7e0	0x2800
553	blk.32.attn_output.weight	0xac67dfe0	0x2a8000
554	blk.32.attn_q.weight	0xac925fe0	0x226000
555	blk.32.attn_q_norm.weight	0xacb4bfe0	0x400
556	blk.32.attn_v.weight	0xacb4c3e0	0x89800
557	blk.32.ffn_down.weight	0xacbd5be0	0x992000
558	blk.32.ffn_gate.weight	0xad567be0	0xabe000
559	blk.32.ffn_norm.weight	0xae025be0	0x2800
560	blk.32.ffn_up.weight	0xae0283e0	0xabe000
561	blk.32.inp_gate.weight	0xaeae63e0	0x55000
562	blk.32.layer_output_scale.weight	0xaeb3b3e0	0x4
563	blk.32.post_attention_norm.weight	0xaeb3b400	0x2800
564	blk.32.post_ffw_norm.weight	0xaeb3dc00	0x2800
565	blk.32.post_norm.weight	0xaeb40400	0x2800
566	blk.32.proj.weight	0xaeb42c00	0x64000
567	blk.33.attn_k.weight	0xaeba6c00	0xb4000
568	blk.33.attn_k_norm.weight	0xaec5ac00	0x400
569	blk.33.attn_norm.weight	0xaec5b000	0x2800
570	blk.33.attn_output.weight	0xaec5d800	0x2a8000
571	blk.33.attn_q.weight	0xaef05800	0x226000
572	blk.33.attn_q_norm.weight	0xaf12b800	0x400
573	blk.33.attn_v.weight	0xaf12bc00	0x89800
574	blk.33.ffn_down.weight	0xaf1b5400	0x992000
575	blk.33.ffn_gate.weight	0xafb47400	0xabe000
576	blk.33.ffn_norm.weight	0xb0605400	0x2800
577	blk.33.ffn_up.weight	0xb0607c00	0xabe000
578	blk.33.inp_gate.weight	0xb10c5c00	0x55000
579	blk.33.layer_output_scale.weight	0xb111ac00	0x4
580	blk.33.post_attention_norm.weight	0xb111ac20	0x2800
581	blk.33.post_ffw_norm.weight	0xb111d420	0x2800
582	blk.33.post_norm.weight	0xb111fc20	0x2800
583	blk.33.proj.weight	0xb1122420	0x64000
584	blk.34.attn_k.weight	0xb1186420	0xb4000
585	blk.34.attn_k_norm.weight	0xb123a420	0x400
586	blk.34.attn_norm.weight	0xb123a820	0x2800
587	blk.34.attn_output.weight	0xb123d020	0x2a8000
588	blk.34.attn_q.weight	0xb14e5020	0x226000
589	blk.34.attn_q_norm.weight	0xb170b020	0x400
590	blk.34.attn_v.weight	0xb170b420	0x89800
591	blk.34.ffn_down.weight	0xb1794c20	0x992000
592	blk.34.ffn_gate.weight	0xb2126c20	0xabe000
593	blk.34.ffn_norm.weight	0xb2be4c20	0x2800
594	blk.34.ffn_up.weight	0xb2be7420	0xabe000
595	blk.34.inp_gate.weight	0xb36a5420	0x55000
596	blk.34.layer_output_scale.weight	0xb36fa420	0x4
597	blk.34.post_attention_norm.weight	0xb36fa440	0x2800
598	blk.34.post_ffw_norm.weight	0xb36fcc40	0x2800
599	blk.34.post_norm.weight	0xb36ff440	0x2800
600	blk.34.proj.weight	0xb3701c40	0x5a000
601	blk.35.attn_k.weight	0xb375bc40	0x168000
602	blk.35.attn_k_norm.weight	0xb38c3c40	0x800
603	blk.35.attn_norm.weight	0xb38c4440	0x2800
604	blk.35.attn_output.weight	0xb38c6c40	0x44c000
605	blk.35.attn_q.weight	0xb3d12c40	0x44c000
606	blk.35.attn_q_norm.weight	0xb415ec40	0x800
607	blk.35.attn_v.weight	0xb415f440	0x113000
608	blk.35.ffn_down.weight	0xb4272440	0xabe000
609	blk.35.ffn_gate.weight	0xb4d30440	0xabe000
610	blk.35.ffn_norm.weight	0xb57ee440	0x2800
611	blk.35.ffn_up.weight	0xb57f0c40	0xabe000
612	blk.35.inp_gate.weight	0xb62aec40	0x3d400
613	blk.35.layer_output_scale.weight	0xb62ec040	0x4
614	blk.35.post_attention_norm.weight	0xb62ec060	0x2800
615	blk.35.post_ffw_norm.weight	0xb62ee860	0x2800
616	blk.35.post_norm.weight	0xb62f1060	0x2800
617	blk.35.proj.weight	0xb62f3860	0x64000
618	blk.36.attn_k.weight	0xb6357860	0xb4000
619	blk.36.attn_k_norm.weight	0xb640b860	0x400
620	blk.36.attn_norm.weight	0xb640bc60	0x2800
621	blk.36.attn_output.weight	0xb640e460	0x2a8000
622	blk.36.attn_q.weight	0xb66b6460	0x226000
623	blk.36.attn_q_norm.weight	0xb68dc460	0x400
624	blk.36.attn_v.weight	0xb68dc860	0x89800
625	blk.36.ffn_down.weight	0xb6966060	0x992000
626	blk.36.ffn_gate.weight	0xb72f8060	0xabe000
627	blk.36.ffn_norm.weight	0xb7db6060	0x2800
628	blk.36.ffn_up.weight	0xb7db8860	0xabe000
629	blk.36.inp_gate.weight	0xb8876860	0x5a000
630	blk.36.layer_output_scale.weight	0xb88d0860	0x4
631	blk.36.post_attention_norm.weight	0xb88d0880	0x2800
632	blk.36.post_ffw_norm.weight	0xb88d3080	0x2800
633	blk.36.post_norm.weight	0xb88d5880	0x2800
634	blk.36.proj.weight	0xb88d8080	0x5a000
635	blk.37.attn_k.weight	0xb8932080	0xb4000
636	blk.37.attn_k_norm.weight	0xb89e6080	0x400
637	blk.37.attn_norm.weight	0xb89e6480	0x2800
638	blk.37.attn_output.weight	0xb89e8c80	0x2a8000
639	blk.37.attn_q.weight	0xb8c90c80	0x226000
640	blk.37.attn_q_norm.weight	0xb8eb6c80	0x400
641	blk.37.attn_v.weight	0xb8eb7080	0x89800
642	blk.37.ffn_down.weight	0xb8f40880	0x992000
643	blk.37.ffn_gate.weight	0xb98d2880	0xabe000
644	blk.37.ffn_norm.weight	0xba390880	0x2800
645	blk.37.ffn_up.weight	0xba393080	0xabe000
646	blk.37.inp_gate.weight	0xbae51080	0x55000
647	blk.37.layer_output_scale.weight	0xbaea6080	0x4
648	blk.37.post_attention_norm.weight	0xbaea60a0	0x2800
649	blk.37.post_ffw_norm.weight	0xbaea88a0	0x2800
650	blk.37.post_norm.weight	0xbaeab0a0	0x2800
651	blk.37.proj.weight	0xbaead8a0	0x64000
652	blk.38.attn_k.weight	0xbaf118a0	0xb4000
653	blk.38.attn_k_norm.weight	0xbafc58a0	0x400
654	blk.38.attn_norm.weight	0xbafc5ca0	0x2800
655	blk.38.attn_output.weight	0xbafc84a0	0x2a8000
656	blk.38.attn_q.weight	0xbb2704a0	0x226000
657	blk.38.attn_q_norm.weight	0xbb4964a0	0x400
658	blk.38.attn_v.weight	0xbb4968a0	0x89800
659	blk.38.ffn_down.weight	0xbb5200a0	0x992000
660	blk.38.ffn_gate.weight	0xbbeb20a0	0xabe000
661	blk.38.ffn_norm.weight	0xbc9700a0	0x2800
662	blk.38.ffn_up.weight	0xbc9728a0	0xabe000
663	blk.38.inp_gate.weight	0xbd4308a0	0x3d400
664	blk.38.layer_output_scale.weight	0xbd46dca0	0x4
665	blk.38.post_attention_norm.weight	0xbd46dcc0	0x2800
666	blk.38.post_ffw_norm.weight	0xbd4704c0	0x2800
667	blk.38.post_norm.weight	0xbd472cc0	0x2800
668	blk.38.proj.weight	0xbd4754c0	0x78000
669	blk.39.attn_k.weight	0xbd4ed4c0	0xb4000
670	blk.39.attn_k_norm.weight	0xbd5a14c0	0x400
671	blk.39.attn_norm.weight	0xbd5a18c0	0x2800
672	blk.39.attn_output.weight	0xbd5a40c0	0x2a8000
673	blk.39.attn_q.weight	0xbd84c0c0	0x226000
674	blk.39.attn_q_norm.weight	0xbda720c0	0x400
675	blk.39.attn_v.weight	0xbda724c0	0x89800
676	blk.39.ffn_down.weight	0xbdafbcc0	0x992000
677	blk.39.ffn_gate.weight	0xbe48dcc0	0xabe000
678	blk.39.ffn_norm.weight	0xbef4bcc0	0x2800
679	blk.39.ffn_up.weight	0xbef4e4c0	0xabe000
680	blk.39.inp_gate.weight	0xbfa0c4c0	0x3d400
681	blk.39.layer_output_scale.weight	0xbfa498c0	0x4
682	blk.39.post_attention_norm.weight	0xbfa498e0	0x2800
683	blk.39.post_ffw_norm.weight	0xbfa4c0e0	0x2800
684	blk.39.post_norm.weight	0xbfa4e8e0	0x2800
685	blk.39.proj.weight	0xbfa510e0	0x64000
686	blk.40.attn_k.weight	0xbfab50e0	0xb4000
687	blk.40.attn_k_norm.weight	0xbfb690e0	0x400
688	blk.40.attn_norm.weight	0xbfb694e0	0x2800
689	blk.40.attn_output.weight	0xbfb6bce0	0x2a8000
690	blk.40.attn_q.weight	0xbfe13ce0	0x226000
691	blk.40.attn_q_norm.weight	0xc0039ce0	0x400
692	blk.40.attn_v.weight	0xc003a0e0	0x89800
693	blk.40.ffn_down.weight	0xc00c38e0	0x992000
694	blk.40.ffn_gate.weight	0xc0a558e0	0xd48000
695	blk.40.ffn_norm.weight	0xc179d8e0	0x2800
696	blk.40.ffn_up.weight	0xc17a00e0	0xd48000
697	blk.40.inp_gate.weight	0xc24e80e0	0x3d400
698	blk.40.layer_output_scale.weight	0xc25254e0	0x4
699	blk.40.post_attention_norm.weight	0xc2525500	0x2800
700	blk.40.post_ffw_norm.weight	0xc2527d00	0x2800
701	blk.40.post_norm.weight	0xc252a500	0x2800
702	blk.40.proj.weight	0xc252cd00	0x64000
703	blk.41.attn_k.weight	0xc2590d00	0x168000
704	blk.41.attn_k_norm.weight	0xc26f8d00	0x800
705	blk.41.attn_norm.weight	0xc26f9500	0x2800
706	blk.41.attn_output.weight	0xc26fbd00	0x44c000
707	blk.41.attn_q.weight	0xc2b47d00	0x3d4000
708	blk.41.attn_q_norm.weight	0xc2f1bd00	0x800
709	blk.41.attn_v.weight	0xc2f1c500	0x113000
710	blk.41.ffn_down.weight	0xc302f500	0x992000
711	blk.41.ffn_gate.weight	0xc39c1500	0xabe000
712	blk.41.ffn_norm.weight	0xc447f500	0x2800
713	blk.41.ffn_up.weight	0xc4481d00	0xabe000
714	blk.41.inp_gate.weight	0xc4f3fd00	0x34800
715	blk.41.layer_output_scale.weight	0xc4f74500	0x4
716	blk.41.post_attention_norm.weight	0xc4f74520	0x2800
717	blk.41.post_ffw_norm.weight	0xc4f76d20	0x2800
718	blk.41.post_norm.weight	0xc4f79520	0x2800
719	blk.41.proj.weight	0xc4f7bd20	0x64000

Base Tensor Group : ~4B Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
0	output_norm.weight	Output Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
1	per_layer_model_proj.weight	Per_Layer_Model_Proj (W)	( ~28M) 27525120	2560 x 10752 x 1 x 1	F16	16.0000
2	per_layer_proj_norm.weight	Per_Layer_Proj_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
3	per_layer_token_embd.weight	Per_Layer_Token_Embd (W)	( ~3B) 2818572288	10752 x 262144 x 1 x 1	Q3_K	3.4375
4	rope_freqs.weight	Rope_Freqs (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
5	token_embd.weight	Token Embedding (W)	(~671M) 671088640	2560 x 262144 x 1 x 1	Q3_K	3.4375

Total elements in base: ( ~4B) 3517189120
Percentage of total elements: 46.78%
Bits per Weight (BPW) for base: 3.5358 bits

Block 0 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
6	blk.0.attn_k.weight	Block 0 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
7	blk.0.attn_k_norm.weight	Block 0 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
8	blk.0.attn_norm.weight	Block 0 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
9	blk.0.attn_output.weight	Block 0 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
10	blk.0.attn_q.weight	Block 0 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
11	blk.0.attn_q_norm.weight	Block 0 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
12	blk.0.attn_v.weight	Block 0 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
13	blk.0.ffn_down.weight	Block 0 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q2_K	2.6250
14	blk.0.ffn_gate.weight	Block 0 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ3_XXS	3.0625
15	blk.0.ffn_norm.weight	Block 0 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
16	blk.0.ffn_up.weight	Block 0 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
17	blk.0.inp_gate.weight	Block 0 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
18	blk.0.layer_output_scale.weight	Block 0 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
19	blk.0.post_attention_norm.weight	Block 0 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
20	blk.0.post_ffw_norm.weight	Block 0 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
21	blk.0.post_norm.weight	Block 0 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
22	blk.0.proj.weight	Block 0 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.0: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.0: 3.1731 bits

Block 1 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
23	blk.1.attn_k.weight	Block 1 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
24	blk.1.attn_k_norm.weight	Block 1 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
25	blk.1.attn_norm.weight	Block 1 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
26	blk.1.attn_output.weight	Block 1 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
27	blk.1.attn_q.weight	Block 1 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
28	blk.1.attn_q_norm.weight	Block 1 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
29	blk.1.attn_v.weight	Block 1 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
30	blk.1.ffn_down.weight	Block 1 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
31	blk.1.ffn_gate.weight	Block 1 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q2_K	2.6250
32	blk.1.ffn_norm.weight	Block 1 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
33	blk.1.ffn_up.weight	Block 1 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
34	blk.1.inp_gate.weight	Block 1 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
35	blk.1.layer_output_scale.weight	Block 1 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
36	blk.1.post_attention_norm.weight	Block 1 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
37	blk.1.post_ffw_norm.weight	Block 1 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
38	blk.1.post_norm.weight	Block 1 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
39	blk.1.proj.weight	Block 1 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.1: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.1: 3.3949 bits

Block 2 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
40	blk.2.attn_k.weight	Block 2 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
41	blk.2.attn_k_norm.weight	Block 2 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
42	blk.2.attn_norm.weight	Block 2 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
43	blk.2.attn_output.weight	Block 2 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
44	blk.2.attn_q.weight	Block 2 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
45	blk.2.attn_q_norm.weight	Block 2 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
46	blk.2.attn_v.weight	Block 2 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
47	blk.2.ffn_down.weight	Block 2 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
48	blk.2.ffn_gate.weight	Block 2 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ3_XXS	3.0625
49	blk.2.ffn_norm.weight	Block 2 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
50	blk.2.ffn_up.weight	Block 2 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
51	blk.2.inp_gate.weight	Block 2 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
52	blk.2.layer_output_scale.weight	Block 2 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
53	blk.2.post_attention_norm.weight	Block 2 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
54	blk.2.post_ffw_norm.weight	Block 2 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
55	blk.2.post_norm.weight	Block 2 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
56	blk.2.proj.weight	Block 2 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.2: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.2: 3.3025 bits

Block 3 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
57	blk.3.attn_k.weight	Block 3 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
58	blk.3.attn_k_norm.weight	Block 3 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
59	blk.3.attn_norm.weight	Block 3 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
60	blk.3.attn_output.weight	Block 3 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
61	blk.3.attn_q.weight	Block 3 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
62	blk.3.attn_q_norm.weight	Block 3 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
63	blk.3.attn_v.weight	Block 3 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
64	blk.3.ffn_down.weight	Block 3 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
65	blk.3.ffn_gate.weight	Block 3 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
66	blk.3.ffn_norm.weight	Block 3 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
67	blk.3.ffn_up.weight	Block 3 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
68	blk.3.inp_gate.weight	Block 3 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
69	blk.3.layer_output_scale.weight	Block 3 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
70	blk.3.post_attention_norm.weight	Block 3 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
71	blk.3.post_ffw_norm.weight	Block 3 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
72	blk.3.post_norm.weight	Block 3 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
73	blk.3.proj.weight	Block 3 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.3: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.3: 3.3980 bits

Block 4 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
74	blk.4.attn_k.weight	Block 4 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
75	blk.4.attn_k_norm.weight	Block 4 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
76	blk.4.attn_norm.weight	Block 4 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
77	blk.4.attn_output.weight	Block 4 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
78	blk.4.attn_q.weight	Block 4 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
79	blk.4.attn_q_norm.weight	Block 4 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
80	blk.4.attn_v.weight	Block 4 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
81	blk.4.ffn_down.weight	Block 4 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
82	blk.4.ffn_gate.weight	Block 4 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q2_K	2.6250
83	blk.4.ffn_norm.weight	Block 4 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
84	blk.4.ffn_up.weight	Block 4 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
85	blk.4.inp_gate.weight	Block 4 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
86	blk.4.layer_output_scale.weight	Block 4 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
87	blk.4.post_attention_norm.weight	Block 4 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
88	blk.4.post_ffw_norm.weight	Block 4 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
89	blk.4.post_norm.weight	Block 4 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
90	blk.4.proj.weight	Block 4 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.4: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.4: 3.1692 bits

Block 5 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
91	blk.5.attn_k.weight	Block 5 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ3_S	3.4375
92	blk.5.attn_k_norm.weight	Block 5 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
93	blk.5.attn_norm.weight	Block 5 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
94	blk.5.attn_output.weight	Block 5 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
95	blk.5.attn_q.weight	Block 5 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_S	3.4375
96	blk.5.attn_q_norm.weight	Block 5 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
97	blk.5.attn_v.weight	Block 5 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ4_XS	4.2500
98	blk.5.ffn_down.weight	Block 5 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
99	blk.5.ffn_gate.weight	Block 5 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q2_K	2.6250
100	blk.5.ffn_norm.weight	Block 5 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
101	blk.5.ffn_up.weight	Block 5 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
102	blk.5.inp_gate.weight	Block 5 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
103	blk.5.layer_output_scale.weight	Block 5 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
104	blk.5.post_attention_norm.weight	Block 5 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
105	blk.5.post_ffw_norm.weight	Block 5 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
106	blk.5.post_norm.weight	Block 5 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
107	blk.5.proj.weight	Block 5 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.5: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.5: 3.3803 bits

Block 6 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
108	blk.6.attn_k.weight	Block 6 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
109	blk.6.attn_k_norm.weight	Block 6 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
110	blk.6.attn_norm.weight	Block 6 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
111	blk.6.attn_output.weight	Block 6 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
112	blk.6.attn_q.weight	Block 6 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
113	blk.6.attn_q_norm.weight	Block 6 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
114	blk.6.attn_v.weight	Block 6 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
115	blk.6.ffn_down.weight	Block 6 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
116	blk.6.ffn_gate.weight	Block 6 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q2_K	2.6250
117	blk.6.ffn_norm.weight	Block 6 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
118	blk.6.ffn_up.weight	Block 6 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
119	blk.6.inp_gate.weight	Block 6 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
120	blk.6.layer_output_scale.weight	Block 6 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
121	blk.6.post_attention_norm.weight	Block 6 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
122	blk.6.post_ffw_norm.weight	Block 6 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
123	blk.6.post_norm.weight	Block 6 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
124	blk.6.proj.weight	Block 6 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.6: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.6: 3.4081 bits

Block 7 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
125	blk.7.attn_k.weight	Block 7 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
126	blk.7.attn_k_norm.weight	Block 7 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
127	blk.7.attn_norm.weight	Block 7 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
128	blk.7.attn_output.weight	Block 7 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
129	blk.7.attn_q.weight	Block 7 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
130	blk.7.attn_q_norm.weight	Block 7 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
131	blk.7.attn_v.weight	Block 7 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
132	blk.7.ffn_down.weight	Block 7 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
133	blk.7.ffn_gate.weight	Block 7 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
134	blk.7.ffn_norm.weight	Block 7 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
135	blk.7.ffn_up.weight	Block 7 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
136	blk.7.inp_gate.weight	Block 7 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
137	blk.7.layer_output_scale.weight	Block 7 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
138	blk.7.post_attention_norm.weight	Block 7 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
139	blk.7.post_ffw_norm.weight	Block 7 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
140	blk.7.post_norm.weight	Block 7 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
141	blk.7.proj.weight	Block 7 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.7: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.7: 3.3967 bits

Block 8 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
142	blk.8.attn_k.weight	Block 8 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
143	blk.8.attn_k_norm.weight	Block 8 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
144	blk.8.attn_norm.weight	Block 8 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
145	blk.8.attn_output.weight	Block 8 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
146	blk.8.attn_q.weight	Block 8 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
147	blk.8.attn_q_norm.weight	Block 8 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
148	blk.8.attn_v.weight	Block 8 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
149	blk.8.ffn_down.weight	Block 8 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
150	blk.8.ffn_gate.weight	Block 8 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
151	blk.8.ffn_norm.weight	Block 8 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
152	blk.8.ffn_up.weight	Block 8 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
153	blk.8.inp_gate.weight	Block 8 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
154	blk.8.layer_output_scale.weight	Block 8 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
155	blk.8.post_attention_norm.weight	Block 8 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
156	blk.8.post_ffw_norm.weight	Block 8 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
157	blk.8.post_norm.weight	Block 8 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
158	blk.8.proj.weight	Block 8 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.8: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.8: 3.3866 bits

Block 9 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
159	blk.9.attn_k.weight	Block 9 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
160	blk.9.attn_k_norm.weight	Block 9 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
161	blk.9.attn_norm.weight	Block 9 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
162	blk.9.attn_output.weight	Block 9 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
163	blk.9.attn_q.weight	Block 9 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
164	blk.9.attn_q_norm.weight	Block 9 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
165	blk.9.attn_v.weight	Block 9 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
166	blk.9.ffn_down.weight	Block 9 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
167	blk.9.ffn_gate.weight	Block 9 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
168	blk.9.ffn_norm.weight	Block 9 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
169	blk.9.ffn_up.weight	Block 9 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
170	blk.9.inp_gate.weight	Block 9 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
171	blk.9.layer_output_scale.weight	Block 9 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
172	blk.9.post_attention_norm.weight	Block 9 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
173	blk.9.post_ffw_norm.weight	Block 9 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
174	blk.9.post_norm.weight	Block 9 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
175	blk.9.proj.weight	Block 9 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.9: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.9: 3.3949 bits

Block 10 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
176	blk.10.attn_k.weight	Block 10 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
177	blk.10.attn_k_norm.weight	Block 10 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
178	blk.10.attn_norm.weight	Block 10 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
179	blk.10.attn_output.weight	Block 10 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
180	blk.10.attn_q.weight	Block 10 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
181	blk.10.attn_q_norm.weight	Block 10 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
182	blk.10.attn_v.weight	Block 10 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
183	blk.10.ffn_down.weight	Block 10 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
184	blk.10.ffn_gate.weight	Block 10 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
185	blk.10.ffn_norm.weight	Block 10 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
186	blk.10.ffn_up.weight	Block 10 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
187	blk.10.inp_gate.weight	Block 10 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
188	blk.10.layer_output_scale.weight	Block 10 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
189	blk.10.post_attention_norm.weight	Block 10 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
190	blk.10.post_ffw_norm.weight	Block 10 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
191	blk.10.post_norm.weight	Block 10 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
192	blk.10.proj.weight	Block 10 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.10: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.10: 3.5190 bits

Block 11 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
193	blk.11.attn_k.weight	Block 11 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q3_K	3.4375
194	blk.11.attn_k_norm.weight	Block 11 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
195	blk.11.attn_norm.weight	Block 11 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
196	blk.11.attn_output.weight	Block 11 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
197	blk.11.attn_q.weight	Block 11 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_S	3.4375
198	blk.11.attn_q_norm.weight	Block 11 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
199	blk.11.attn_v.weight	Block 11 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ4_XS	4.2500
200	blk.11.ffn_down.weight	Block 11 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
201	blk.11.ffn_gate.weight	Block 11 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
202	blk.11.ffn_norm.weight	Block 11 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
203	blk.11.ffn_up.weight	Block 11 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
204	blk.11.inp_gate.weight	Block 11 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
205	blk.11.layer_output_scale.weight	Block 11 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
206	blk.11.post_attention_norm.weight	Block 11 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
207	blk.11.post_ffw_norm.weight	Block 11 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
208	blk.11.post_norm.weight	Block 11 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
209	blk.11.proj.weight	Block 11 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.11: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.11: 3.3729 bits

Block 12 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
210	blk.12.attn_k.weight	Block 12 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
211	blk.12.attn_k_norm.weight	Block 12 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
212	blk.12.attn_norm.weight	Block 12 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
213	blk.12.attn_output.weight	Block 12 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
214	blk.12.attn_q.weight	Block 12 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
215	blk.12.attn_q_norm.weight	Block 12 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
216	blk.12.attn_v.weight	Block 12 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
217	blk.12.ffn_down.weight	Block 12 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
218	blk.12.ffn_gate.weight	Block 12 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
219	blk.12.ffn_norm.weight	Block 12 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
220	blk.12.ffn_up.weight	Block 12 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
221	blk.12.inp_gate.weight	Block 12 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
222	blk.12.layer_output_scale.weight	Block 12 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
223	blk.12.post_attention_norm.weight	Block 12 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
224	blk.12.post_ffw_norm.weight	Block 12 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
225	blk.12.post_norm.weight	Block 12 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
226	blk.12.proj.weight	Block 12 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.12: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.12: 3.3980 bits

Block 13 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
227	blk.13.attn_k.weight	Block 13 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
228	blk.13.attn_k_norm.weight	Block 13 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
229	blk.13.attn_norm.weight	Block 13 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
230	blk.13.attn_output.weight	Block 13 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
231	blk.13.attn_q.weight	Block 13 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
232	blk.13.attn_q_norm.weight	Block 13 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
233	blk.13.attn_v.weight	Block 13 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
234	blk.13.ffn_down.weight	Block 13 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
235	blk.13.ffn_gate.weight	Block 13 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
236	blk.13.ffn_norm.weight	Block 13 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
237	blk.13.ffn_up.weight	Block 13 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
238	blk.13.inp_gate.weight	Block 13 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
239	blk.13.layer_output_scale.weight	Block 13 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
240	blk.13.post_attention_norm.weight	Block 13 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
241	blk.13.post_ffw_norm.weight	Block 13 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
242	blk.13.post_norm.weight	Block 13 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
243	blk.13.proj.weight	Block 13 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q8_0	8.5000

Total elements in blk.13: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.13: 3.4834 bits

Block 14 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
244	blk.14.attn_k.weight	Block 14 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
245	blk.14.attn_k_norm.weight	Block 14 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
246	blk.14.attn_norm.weight	Block 14 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
247	blk.14.attn_output.weight	Block 14 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
248	blk.14.attn_q.weight	Block 14 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
249	blk.14.attn_q_norm.weight	Block 14 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
250	blk.14.attn_v.weight	Block 14 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
251	blk.14.ffn_down.weight	Block 14 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
252	blk.14.ffn_gate.weight	Block 14 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
253	blk.14.ffn_norm.weight	Block 14 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
254	blk.14.ffn_up.weight	Block 14 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
255	blk.14.inp_gate.weight	Block 14 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
256	blk.14.layer_output_scale.weight	Block 14 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
257	blk.14.post_attention_norm.weight	Block 14 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
258	blk.14.post_ffw_norm.weight	Block 14 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
259	blk.14.post_norm.weight	Block 14 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
260	blk.14.proj.weight	Block 14 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.14: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.14: 3.4169 bits

Block 15 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
261	blk.15.attn_k.weight	Block 15 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
262	blk.15.attn_k_norm.weight	Block 15 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
263	blk.15.attn_norm.weight	Block 15 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
264	blk.15.attn_output.weight	Block 15 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
265	blk.15.attn_q.weight	Block 15 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
266	blk.15.attn_q_norm.weight	Block 15 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
267	blk.15.attn_v.weight	Block 15 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
268	blk.15.ffn_down.weight	Block 15 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
269	blk.15.ffn_gate.weight	Block 15 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
270	blk.15.ffn_norm.weight	Block 15 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
271	blk.15.ffn_up.weight	Block 15 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
272	blk.15.inp_gate.weight	Block 15 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
273	blk.15.layer_output_scale.weight	Block 15 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
274	blk.15.post_attention_norm.weight	Block 15 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
275	blk.15.post_ffw_norm.weight	Block 15 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
276	blk.15.post_norm.weight	Block 15 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
277	blk.15.proj.weight	Block 15 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.15: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.15: 3.4051 bits

Block 16 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
278	blk.16.attn_k.weight	Block 16 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ3_S	3.4375
279	blk.16.attn_k_norm.weight	Block 16 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
280	blk.16.attn_norm.weight	Block 16 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
281	blk.16.attn_output.weight	Block 16 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
282	blk.16.attn_q.weight	Block 16 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
283	blk.16.attn_q_norm.weight	Block 16 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
284	blk.16.attn_v.weight	Block 16 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
285	blk.16.ffn_down.weight	Block 16 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
286	blk.16.ffn_gate.weight	Block 16 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
287	blk.16.ffn_norm.weight	Block 16 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
288	blk.16.ffn_up.weight	Block 16 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
289	blk.16.inp_gate.weight	Block 16 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
290	blk.16.layer_output_scale.weight	Block 16 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
291	blk.16.post_attention_norm.weight	Block 16 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
292	blk.16.post_ffw_norm.weight	Block 16 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
293	blk.16.post_norm.weight	Block 16 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
294	blk.16.proj.weight	Block 16 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.16: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.16: 3.5072 bits

Block 17 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
295	blk.17.attn_k.weight	Block 17 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ3_S	3.4375
296	blk.17.attn_k_norm.weight	Block 17 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
297	blk.17.attn_norm.weight	Block 17 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
298	blk.17.attn_output.weight	Block 17 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
299	blk.17.attn_q.weight	Block 17 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_S	3.4375
300	blk.17.attn_q_norm.weight	Block 17 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
301	blk.17.attn_v.weight	Block 17 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ4_XS	4.2500
302	blk.17.ffn_down.weight	Block 17 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
303	blk.17.ffn_gate.weight	Block 17 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
304	blk.17.ffn_norm.weight	Block 17 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
305	blk.17.ffn_up.weight	Block 17 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
306	blk.17.inp_gate.weight	Block 17 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
307	blk.17.layer_output_scale.weight	Block 17 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
308	blk.17.post_attention_norm.weight	Block 17 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
309	blk.17.post_ffw_norm.weight	Block 17 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
310	blk.17.post_norm.weight	Block 17 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
311	blk.17.proj.weight	Block 17 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.17: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.17: 3.4759 bits

Block 18 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
312	blk.18.attn_k.weight	Block 18 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
313	blk.18.attn_k_norm.weight	Block 18 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
314	blk.18.attn_norm.weight	Block 18 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
315	blk.18.attn_output.weight	Block 18 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
316	blk.18.attn_q.weight	Block 18 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
317	blk.18.attn_q_norm.weight	Block 18 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
318	blk.18.attn_v.weight	Block 18 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
319	blk.18.ffn_down.weight	Block 18 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
320	blk.18.ffn_gate.weight	Block 18 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
321	blk.18.ffn_norm.weight	Block 18 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
322	blk.18.ffn_up.weight	Block 18 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
323	blk.18.inp_gate.weight	Block 18 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
324	blk.18.layer_output_scale.weight	Block 18 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
325	blk.18.post_attention_norm.weight	Block 18 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
326	blk.18.post_ffw_norm.weight	Block 18 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
327	blk.18.post_norm.weight	Block 18 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
328	blk.18.proj.weight	Block 18 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.18: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.18: 3.5815 bits

Block 19 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
329	blk.19.attn_k.weight	Block 19 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
330	blk.19.attn_k_norm.weight	Block 19 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
331	blk.19.attn_norm.weight	Block 19 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
332	blk.19.attn_output.weight	Block 19 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
333	blk.19.attn_q.weight	Block 19 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
334	blk.19.attn_q_norm.weight	Block 19 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
335	blk.19.attn_v.weight	Block 19 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
336	blk.19.ffn_down.weight	Block 19 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
337	blk.19.ffn_gate.weight	Block 19 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
338	blk.19.ffn_norm.weight	Block 19 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
339	blk.19.ffn_up.weight	Block 19 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
340	blk.19.inp_gate.weight	Block 19 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
341	blk.19.layer_output_scale.weight	Block 19 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
342	blk.19.post_attention_norm.weight	Block 19 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
343	blk.19.post_ffw_norm.weight	Block 19 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
344	blk.19.post_norm.weight	Block 19 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
345	blk.19.proj.weight	Block 19 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.19: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.19: 3.5234 bits

Block 20 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
346	blk.20.attn_k.weight	Block 20 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
347	blk.20.attn_k_norm.weight	Block 20 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
348	blk.20.attn_norm.weight	Block 20 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
349	blk.20.attn_output.weight	Block 20 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
350	blk.20.attn_q.weight	Block 20 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
351	blk.20.attn_q_norm.weight	Block 20 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
352	blk.20.attn_v.weight	Block 20 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
353	blk.20.ffn_down.weight	Block 20 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
354	blk.20.ffn_gate.weight	Block 20 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
355	blk.20.ffn_norm.weight	Block 20 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
356	blk.20.ffn_up.weight	Block 20 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
357	blk.20.inp_gate.weight	Block 20 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
358	blk.20.layer_output_scale.weight	Block 20 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
359	blk.20.post_attention_norm.weight	Block 20 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
360	blk.20.post_ffw_norm.weight	Block 20 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
361	blk.20.post_norm.weight	Block 20 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
362	blk.20.proj.weight	Block 20 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.20: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.20: 3.5608 bits

Block 21 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
363	blk.21.attn_k.weight	Block 21 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
364	blk.21.attn_k_norm.weight	Block 21 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
365	blk.21.attn_norm.weight	Block 21 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
366	blk.21.attn_output.weight	Block 21 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
367	blk.21.attn_q.weight	Block 21 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
368	blk.21.attn_q_norm.weight	Block 21 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
369	blk.21.attn_v.weight	Block 21 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
370	blk.21.ffn_down.weight	Block 21 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
371	blk.21.ffn_gate.weight	Block 21 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
372	blk.21.ffn_norm.weight	Block 21 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
373	blk.21.ffn_up.weight	Block 21 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
374	blk.21.inp_gate.weight	Block 21 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
375	blk.21.layer_output_scale.weight	Block 21 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
376	blk.21.post_attention_norm.weight	Block 21 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
377	blk.21.post_ffw_norm.weight	Block 21 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
378	blk.21.post_norm.weight	Block 21 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
379	blk.21.proj.weight	Block 21 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.21: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.21: 3.7967 bits

Block 22 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
380	blk.22.attn_k.weight	Block 22 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
381	blk.22.attn_k_norm.weight	Block 22 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
382	blk.22.attn_norm.weight	Block 22 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
383	blk.22.attn_output.weight	Block 22 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
384	blk.22.attn_q.weight	Block 22 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
385	blk.22.attn_q_norm.weight	Block 22 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
386	blk.22.attn_v.weight	Block 22 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	IQ4_XS	4.2500
387	blk.22.ffn_down.weight	Block 22 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
388	blk.22.ffn_gate.weight	Block 22 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
389	blk.22.ffn_norm.weight	Block 22 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
390	blk.22.ffn_up.weight	Block 22 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
391	blk.22.inp_gate.weight	Block 22 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
392	blk.22.layer_output_scale.weight	Block 22 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
393	blk.22.post_attention_norm.weight	Block 22 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
394	blk.22.post_ffw_norm.weight	Block 22 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
395	blk.22.post_norm.weight	Block 22 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
396	blk.22.proj.weight	Block 22 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.22: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.22: 3.4521 bits

Block 23 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
397	blk.23.attn_k.weight	Block 23 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ4_XS	4.2500
398	blk.23.attn_k_norm.weight	Block 23 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
399	blk.23.attn_norm.weight	Block 23 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
400	blk.23.attn_output.weight	Block 23 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
401	blk.23.attn_q.weight	Block 23 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ4_XS	4.2500
402	blk.23.attn_q_norm.weight	Block 23 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
403	blk.23.attn_v.weight	Block 23 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	IQ4_XS	4.2500
404	blk.23.ffn_down.weight	Block 23 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
405	blk.23.ffn_gate.weight	Block 23 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
406	blk.23.ffn_norm.weight	Block 23 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
407	blk.23.ffn_up.weight	Block 23 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
408	blk.23.inp_gate.weight	Block 23 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
409	blk.23.layer_output_scale.weight	Block 23 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
410	blk.23.post_attention_norm.weight	Block 23 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
411	blk.23.post_ffw_norm.weight	Block 23 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
412	blk.23.post_norm.weight	Block 23 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
413	blk.23.proj.weight	Block 23 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.23: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.23: 3.7768 bits

Block 24 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
414	blk.24.attn_k.weight	Block 24 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
415	blk.24.attn_k_norm.weight	Block 24 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
416	blk.24.attn_norm.weight	Block 24 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
417	blk.24.attn_output.weight	Block 24 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
418	blk.24.attn_q.weight	Block 24 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
419	blk.24.attn_q_norm.weight	Block 24 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
420	blk.24.attn_v.weight	Block 24 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
421	blk.24.ffn_down.weight	Block 24 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
422	blk.24.ffn_gate.weight	Block 24 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
423	blk.24.ffn_norm.weight	Block 24 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
424	blk.24.ffn_up.weight	Block 24 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
425	blk.24.inp_gate.weight	Block 24 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
426	blk.24.layer_output_scale.weight	Block 24 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
427	blk.24.post_attention_norm.weight	Block 24 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
428	blk.24.post_ffw_norm.weight	Block 24 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
429	blk.24.post_norm.weight	Block 24 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
430	blk.24.proj.weight	Block 24 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.24: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.24: 3.7901 bits

Block 25 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
431	blk.25.attn_k.weight	Block 25 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
432	blk.25.attn_k_norm.weight	Block 25 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
433	blk.25.attn_norm.weight	Block 25 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
434	blk.25.attn_output.weight	Block 25 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
435	blk.25.attn_q.weight	Block 25 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
436	blk.25.attn_q_norm.weight	Block 25 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
437	blk.25.attn_v.weight	Block 25 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
438	blk.25.ffn_down.weight	Block 25 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
439	blk.25.ffn_gate.weight	Block 25 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
440	blk.25.ffn_norm.weight	Block 25 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
441	blk.25.ffn_up.weight	Block 25 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
442	blk.25.inp_gate.weight	Block 25 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
443	blk.25.layer_output_scale.weight	Block 25 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
444	blk.25.post_attention_norm.weight	Block 25 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
445	blk.25.post_ffw_norm.weight	Block 25 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
446	blk.25.post_norm.weight	Block 25 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
447	blk.25.proj.weight	Block 25 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.25: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.25: 3.5190 bits

Block 26 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
448	blk.26.attn_k.weight	Block 26 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
449	blk.26.attn_k_norm.weight	Block 26 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
450	blk.26.attn_norm.weight	Block 26 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
451	blk.26.attn_output.weight	Block 26 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
452	blk.26.attn_q.weight	Block 26 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
453	blk.26.attn_q_norm.weight	Block 26 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
454	blk.26.attn_v.weight	Block 26 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
455	blk.26.ffn_down.weight	Block 26 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
456	blk.26.ffn_gate.weight	Block 26 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
457	blk.26.ffn_norm.weight	Block 26 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
458	blk.26.ffn_up.weight	Block 26 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
459	blk.26.inp_gate.weight	Block 26 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
460	blk.26.layer_output_scale.weight	Block 26 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
461	blk.26.post_attention_norm.weight	Block 26 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
462	blk.26.post_ffw_norm.weight	Block 26 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
463	blk.26.post_norm.weight	Block 26 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
464	blk.26.proj.weight	Block 26 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.26: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.26: 3.5111 bits

Block 27 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
465	blk.27.attn_k.weight	Block 27 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
466	blk.27.attn_k_norm.weight	Block 27 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
467	blk.27.attn_norm.weight	Block 27 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
468	blk.27.attn_output.weight	Block 27 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
469	blk.27.attn_q.weight	Block 27 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
470	blk.27.attn_q_norm.weight	Block 27 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
471	blk.27.attn_v.weight	Block 27 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
472	blk.27.ffn_down.weight	Block 27 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
473	blk.27.ffn_gate.weight	Block 27 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
474	blk.27.ffn_norm.weight	Block 27 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
475	blk.27.ffn_up.weight	Block 27 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
476	blk.27.inp_gate.weight	Block 27 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
477	blk.27.layer_output_scale.weight	Block 27 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
478	blk.27.post_attention_norm.weight	Block 27 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
479	blk.27.post_ffw_norm.weight	Block 27 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
480	blk.27.post_norm.weight	Block 27 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
481	blk.27.proj.weight	Block 27 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.27: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.27: 3.5261 bits

Block 28 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
482	blk.28.attn_k.weight	Block 28 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
483	blk.28.attn_k_norm.weight	Block 28 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
484	blk.28.attn_norm.weight	Block 28 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
485	blk.28.attn_output.weight	Block 28 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
486	blk.28.attn_q.weight	Block 28 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
487	blk.28.attn_q_norm.weight	Block 28 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
488	blk.28.attn_v.weight	Block 28 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
489	blk.28.ffn_down.weight	Block 28 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
490	blk.28.ffn_gate.weight	Block 28 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
491	blk.28.ffn_norm.weight	Block 28 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
492	blk.28.ffn_up.weight	Block 28 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
493	blk.28.inp_gate.weight	Block 28 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q3_K	3.4375
494	blk.28.layer_output_scale.weight	Block 28 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
495	blk.28.post_attention_norm.weight	Block 28 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
496	blk.28.post_ffw_norm.weight	Block 28 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
497	blk.28.post_norm.weight	Block 28 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
498	blk.28.proj.weight	Block 28 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.28: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.28: 3.5204 bits

Block 29 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
499	blk.29.attn_k.weight	Block 29 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q4_K	4.5000
500	blk.29.attn_k_norm.weight	Block 29 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
501	blk.29.attn_norm.weight	Block 29 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
502	blk.29.attn_output.weight	Block 29 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
503	blk.29.attn_q.weight	Block 29 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_S	3.4375
504	blk.29.attn_q_norm.weight	Block 29 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
505	blk.29.attn_v.weight	Block 29 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q3_K	3.4375
506	blk.29.ffn_down.weight	Block 29 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
507	blk.29.ffn_gate.weight	Block 29 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
508	blk.29.ffn_norm.weight	Block 29 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
509	blk.29.ffn_up.weight	Block 29 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
510	blk.29.inp_gate.weight	Block 29 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
511	blk.29.layer_output_scale.weight	Block 29 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
512	blk.29.post_attention_norm.weight	Block 29 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
513	blk.29.post_ffw_norm.weight	Block 29 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
514	blk.29.post_norm.weight	Block 29 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
515	blk.29.proj.weight	Block 29 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_K	5.5000

Total elements in blk.29: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.29: 3.4852 bits

Block 30 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
516	blk.30.attn_k.weight	Block 30 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
517	blk.30.attn_k_norm.weight	Block 30 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
518	blk.30.attn_norm.weight	Block 30 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
519	blk.30.attn_output.weight	Block 30 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
520	blk.30.attn_q.weight	Block 30 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ4_XS	4.2500
521	blk.30.attn_q_norm.weight	Block 30 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
522	blk.30.attn_v.weight	Block 30 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
523	blk.30.ffn_down.weight	Block 30 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
524	blk.30.ffn_gate.weight	Block 30 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
525	blk.30.ffn_norm.weight	Block 30 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
526	blk.30.ffn_up.weight	Block 30 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
527	blk.30.inp_gate.weight	Block 30 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
528	blk.30.layer_output_scale.weight	Block 30 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
529	blk.30.post_attention_norm.weight	Block 30 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
530	blk.30.post_ffw_norm.weight	Block 30 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
531	blk.30.post_norm.weight	Block 30 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
532	blk.30.proj.weight	Block 30 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.30: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.30: 3.5613 bits

Block 31 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
533	blk.31.attn_k.weight	Block 31 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
534	blk.31.attn_k_norm.weight	Block 31 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
535	blk.31.attn_norm.weight	Block 31 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
536	blk.31.attn_output.weight	Block 31 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
537	blk.31.attn_q.weight	Block 31 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
538	blk.31.attn_q_norm.weight	Block 31 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
539	blk.31.attn_v.weight	Block 31 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
540	blk.31.ffn_down.weight	Block 31 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
541	blk.31.ffn_gate.weight	Block 31 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
542	blk.31.ffn_norm.weight	Block 31 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
543	blk.31.ffn_up.weight	Block 31 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
544	blk.31.inp_gate.weight	Block 31 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
545	blk.31.layer_output_scale.weight	Block 31 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
546	blk.31.post_attention_norm.weight	Block 31 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
547	blk.31.post_ffw_norm.weight	Block 31 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
548	blk.31.post_norm.weight	Block 31 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
549	blk.31.proj.weight	Block 31 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.31: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.31: 3.5261 bits

Block 32 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
550	blk.32.attn_k.weight	Block 32 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
551	blk.32.attn_k_norm.weight	Block 32 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
552	blk.32.attn_norm.weight	Block 32 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
553	blk.32.attn_output.weight	Block 32 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
554	blk.32.attn_q.weight	Block 32 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
555	blk.32.attn_q_norm.weight	Block 32 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
556	blk.32.attn_v.weight	Block 32 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
557	blk.32.ffn_down.weight	Block 32 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
558	blk.32.ffn_gate.weight	Block 32 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
559	blk.32.ffn_norm.weight	Block 32 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
560	blk.32.ffn_up.weight	Block 32 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
561	blk.32.inp_gate.weight	Block 32 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
562	blk.32.layer_output_scale.weight	Block 32 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
563	blk.32.post_attention_norm.weight	Block 32 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
564	blk.32.post_ffw_norm.weight	Block 32 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
565	blk.32.post_norm.weight	Block 32 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
566	blk.32.proj.weight	Block 32 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.32: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.32: 3.4134 bits

Block 33 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
567	blk.33.attn_k.weight	Block 33 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
568	blk.33.attn_k_norm.weight	Block 33 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
569	blk.33.attn_norm.weight	Block 33 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
570	blk.33.attn_output.weight	Block 33 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
571	blk.33.attn_q.weight	Block 33 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
572	blk.33.attn_q_norm.weight	Block 33 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
573	blk.33.attn_v.weight	Block 33 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
574	blk.33.ffn_down.weight	Block 33 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
575	blk.33.ffn_gate.weight	Block 33 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
576	blk.33.ffn_norm.weight	Block 33 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
577	blk.33.ffn_up.weight	Block 33 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
578	blk.33.inp_gate.weight	Block 33 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
579	blk.33.layer_output_scale.weight	Block 33 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
580	blk.33.post_attention_norm.weight	Block 33 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
581	blk.33.post_ffw_norm.weight	Block 33 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
582	blk.33.post_norm.weight	Block 33 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
583	blk.33.proj.weight	Block 33 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.33: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.33: 3.4134 bits

Block 34 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
584	blk.34.attn_k.weight	Block 34 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
585	blk.34.attn_k_norm.weight	Block 34 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
586	blk.34.attn_norm.weight	Block 34 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
587	blk.34.attn_output.weight	Block 34 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
588	blk.34.attn_q.weight	Block 34 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
589	blk.34.attn_q_norm.weight	Block 34 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
590	blk.34.attn_v.weight	Block 34 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
591	blk.34.ffn_down.weight	Block 34 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
592	blk.34.ffn_gate.weight	Block 34 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
593	blk.34.ffn_norm.weight	Block 34 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
594	blk.34.ffn_up.weight	Block 34 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
595	blk.34.inp_gate.weight	Block 34 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
596	blk.34.layer_output_scale.weight	Block 34 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
597	blk.34.post_attention_norm.weight	Block 34 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
598	blk.34.post_ffw_norm.weight	Block 34 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
599	blk.34.post_norm.weight	Block 34 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
600	blk.34.proj.weight	Block 34 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.34: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.34: 3.4099 bits

Block 35 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
601	blk.35.attn_k.weight	Block 35 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q4_K	4.5000
602	blk.35.attn_k_norm.weight	Block 35 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
603	blk.35.attn_norm.weight	Block 35 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
604	blk.35.attn_output.weight	Block 35 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
605	blk.35.attn_q.weight	Block 35 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_S	3.4375
606	blk.35.attn_q_norm.weight	Block 35 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
607	blk.35.attn_v.weight	Block 35 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q3_K	3.4375
608	blk.35.ffn_down.weight	Block 35 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q3_K	3.4375
609	blk.35.ffn_gate.weight	Block 35 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
610	blk.35.ffn_norm.weight	Block 35 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
611	blk.35.ffn_up.weight	Block 35 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
612	blk.35.inp_gate.weight	Block 35 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
613	blk.35.layer_output_scale.weight	Block 35 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
614	blk.35.post_attention_norm.weight	Block 35 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
615	blk.35.post_ffw_norm.weight	Block 35 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
616	blk.35.post_norm.weight	Block 35 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
617	blk.35.proj.weight	Block 35 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.35: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.35: 3.4748 bits

Block 36 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
618	blk.36.attn_k.weight	Block 36 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
619	blk.36.attn_k_norm.weight	Block 36 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
620	blk.36.attn_norm.weight	Block 36 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
621	blk.36.attn_output.weight	Block 36 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
622	blk.36.attn_q.weight	Block 36 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
623	blk.36.attn_q_norm.weight	Block 36 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
624	blk.36.attn_v.weight	Block 36 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
625	blk.36.ffn_down.weight	Block 36 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
626	blk.36.ffn_gate.weight	Block 36 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
627	blk.36.ffn_norm.weight	Block 36 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
628	blk.36.ffn_up.weight	Block 36 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
629	blk.36.inp_gate.weight	Block 36 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
630	blk.36.layer_output_scale.weight	Block 36 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
631	blk.36.post_attention_norm.weight	Block 36 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
632	blk.36.post_ffw_norm.weight	Block 36 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
633	blk.36.post_norm.weight	Block 36 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
634	blk.36.proj.weight	Block 36 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_K	4.5000

Total elements in blk.36: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.36: 3.4117 bits

Block 37 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
635	blk.37.attn_k.weight	Block 37 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
636	blk.37.attn_k_norm.weight	Block 37 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
637	blk.37.attn_norm.weight	Block 37 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
638	blk.37.attn_output.weight	Block 37 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
639	blk.37.attn_q.weight	Block 37 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
640	blk.37.attn_q_norm.weight	Block 37 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
641	blk.37.attn_v.weight	Block 37 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
642	blk.37.ffn_down.weight	Block 37 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
643	blk.37.ffn_gate.weight	Block 37 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
644	blk.37.ffn_norm.weight	Block 37 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
645	blk.37.ffn_up.weight	Block 37 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
646	blk.37.inp_gate.weight	Block 37 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ4_XS	4.2500
647	blk.37.layer_output_scale.weight	Block 37 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
648	blk.37.post_attention_norm.weight	Block 37 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
649	blk.37.post_ffw_norm.weight	Block 37 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
650	blk.37.post_norm.weight	Block 37 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
651	blk.37.proj.weight	Block 37 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.37: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.37: 3.4134 bits

Block 38 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
652	blk.38.attn_k.weight	Block 38 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
653	blk.38.attn_k_norm.weight	Block 38 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
654	blk.38.attn_norm.weight	Block 38 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
655	blk.38.attn_output.weight	Block 38 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
656	blk.38.attn_q.weight	Block 38 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
657	blk.38.attn_q_norm.weight	Block 38 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
658	blk.38.attn_v.weight	Block 38 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
659	blk.38.ffn_down.weight	Block 38 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
660	blk.38.ffn_gate.weight	Block 38 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
661	blk.38.ffn_norm.weight	Block 38 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
662	blk.38.ffn_up.weight	Block 38 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
663	blk.38.inp_gate.weight	Block 38 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
664	blk.38.layer_output_scale.weight	Block 38 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
665	blk.38.post_attention_norm.weight	Block 38 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
666	blk.38.post_ffw_norm.weight	Block 38 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
667	blk.38.post_norm.weight	Block 38 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
668	blk.38.proj.weight	Block 38 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q5_1	6.0000

Total elements in blk.38: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.38: 3.4121 bits

Block 39 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
669	blk.39.attn_k.weight	Block 39 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
670	blk.39.attn_k_norm.weight	Block 39 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
671	blk.39.attn_norm.weight	Block 39 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
672	blk.39.attn_output.weight	Block 39 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
673	blk.39.attn_q.weight	Block 39 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
674	blk.39.attn_q_norm.weight	Block 39 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
675	blk.39.attn_v.weight	Block 39 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
676	blk.39.ffn_down.weight	Block 39 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
677	blk.39.ffn_gate.weight	Block 39 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
678	blk.39.ffn_norm.weight	Block 39 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
679	blk.39.ffn_up.weight	Block 39 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
680	blk.39.inp_gate.weight	Block 39 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
681	blk.39.layer_output_scale.weight	Block 39 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
682	blk.39.post_attention_norm.weight	Block 39 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
683	blk.39.post_ffw_norm.weight	Block 39 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
684	blk.39.post_norm.weight	Block 39 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
685	blk.39.proj.weight	Block 39 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.39: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.39: 3.4051 bits

Block 40 Tensor Group : ~93M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
686	blk.40.attn_k.weight	Block 40 Attention Key (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q4_K	4.5000
687	blk.40.attn_k_norm.weight	Block 40 Attn_K_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
688	blk.40.attn_norm.weight	Block 40 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
689	blk.40.attn_output.weight	Block 40 Attention Output (W)	( ~5M) 5242880	2048 x 2560 x 1 x 1	IQ4_XS	4.2500
690	blk.40.attn_q.weight	Block 40 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	IQ3_S	3.4375
691	blk.40.attn_q_norm.weight	Block 40 Attn_Q_Norm (W)	( 256) 256	256 x 1 x 1 x 1	F32	32.0000
692	blk.40.attn_v.weight	Block 40 Attention Value (W)	( ~1M) 1310720	2560 x 512 x 1 x 1	Q3_K	3.4375
693	blk.40.ffn_down.weight	Block 40 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
694	blk.40.ffn_gate.weight	Block 40 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
695	blk.40.ffn_norm.weight	Block 40 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
696	blk.40.ffn_up.weight	Block 40 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	IQ4_XS	4.2500
697	blk.40.inp_gate.weight	Block 40 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	IQ3_XXS	3.0625
698	blk.40.layer_output_scale.weight	Block 40 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
699	blk.40.post_attention_norm.weight	Block 40 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
700	blk.40.post_ffw_norm.weight	Block 40 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
701	blk.40.post_norm.weight	Block 40 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
702	blk.40.proj.weight	Block 40 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.40: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.40: 3.8627 bits

Block 41 Tensor Group : ~106M Elements

T_ID	Tensor Layer Name	Human Friendly Tensor Layer Name	Elements	Shape	Type	BPW
703	blk.41.attn_k.weight	Block 41 Attention Key (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q4_K	4.5000
704	blk.41.attn_k_norm.weight	Block 41 Attn_K_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
705	blk.41.attn_norm.weight	Block 41 Attention Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
706	blk.41.attn_output.weight	Block 41 Attention Output (W)	( ~10M) 10485760	4096 x 2560 x 1 x 1	Q3_K	3.4375
707	blk.41.attn_q.weight	Block 41 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	IQ3_XXS	3.0625
708	blk.41.attn_q_norm.weight	Block 41 Attn_Q_Norm (W)	( 512) 512	512 x 1 x 1 x 1	F32	32.0000
709	blk.41.attn_v.weight	Block 41 Attention Value (W)	( ~3M) 2621440	2560 x 1024 x 1 x 1	Q3_K	3.4375
710	blk.41.ffn_down.weight	Block 41 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	IQ3_XXS	3.0625
711	blk.41.ffn_gate.weight	Block 41 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
712	blk.41.ffn_norm.weight	Block 41 Feed-Forward Network Normalization (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
713	blk.41.ffn_up.weight	Block 41 Feed-Forward Network "Up" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q3_K	3.4375
714	blk.41.inp_gate.weight	Block 41 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q2_K	2.6250
715	blk.41.layer_output_scale.weight	Block 41 Layer_Output_Scale (W)	( 1) 1	1 x 1 x 1 x 1	F32	32.0000
716	blk.41.post_attention_norm.weight	Block 41 Post_Attention_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
717	blk.41.post_ffw_norm.weight	Block 41 Post_Ffw_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
718	blk.41.post_norm.weight	Block 41 Post_Norm (W)	( ~3K) 2560	2560 x 1 x 1 x 1	F32	32.0000
719	blk.41.proj.weight	Block 41 Proj (W)	(~655K) 655360	256 x 2560 x 1 x 1	Q4_1	5.0000

Total elements in blk.41: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.41: 3.3425 bits

Total BPW for gemma-4-E4B-it-Q3_K.gguf: 3.5000 bits