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-Q5_K.md

eaddario

Add GGUF internal file structure

8399b2d verified 4 months ago

preview code

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204 kB

gemma-4-E4B-it-Q5_K.gguf - GGUF Internal File Dump

Endian: LITTLE endian

Key Value Metadata Store

There are 52 key-value pairs in this file

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44	STRING	1	tokenizer.chat_template	`{%- macro format_parameters(pr`... `{%- endif -%} {%- endif -%}`
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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-Q5_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

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488	blk.28.attn_v.weight	0xfa4aaf60	0x106800
489	blk.28.ffn_down.weight	0xfa5b1760	0x1130000
490	blk.28.ffn_gate.weight	0xfb6e1760	0x1130000
491	blk.28.ffn_norm.weight	0xfc811760	0x2800
492	blk.28.ffn_up.weight	0xfc813f60	0x1482000
493	blk.28.inp_gate.weight	0xfdc95f60	0x6e000
494	blk.28.layer_output_scale.weight	0xfdd03f60	0x4
495	blk.28.post_attention_norm.weight	0xfdd03f80	0x2800
496	blk.28.post_ffw_norm.weight	0xfdd06780	0x2800
497	blk.28.post_norm.weight	0xfdd08f80	0x2800
498	blk.28.proj.weight	0xfdd0b780	0x140000
499	blk.29.attn_k.weight	0xfde4b780	0x20d000
500	blk.29.attn_k_norm.weight	0xfe058780	0x800
501	blk.29.attn_norm.weight	0xfe058f80	0x2800
502	blk.29.attn_output.weight	0xfe05b780	0x6e0000
503	blk.29.attn_q.weight	0xfe73b780	0x6e0000
504	blk.29.attn_q_norm.weight	0xfee1b780	0x800
505	blk.29.attn_v.weight	0xfee1bf80	0x20d000
506	blk.29.ffn_down.weight	0xff028f80	0x1130000
507	blk.29.ffn_gate.weight	0x100158f80	0x1130000
508	blk.29.ffn_norm.weight	0x101288f80	0x2800
509	blk.29.ffn_up.weight	0x10128b780	0x1482000
510	blk.29.inp_gate.weight	0x10270d780	0x83400
511	blk.29.layer_output_scale.weight	0x102790b80	0x4
512	blk.29.post_attention_norm.weight	0x102790ba0	0x2800
513	blk.29.post_ffw_norm.weight	0x1027933a0	0x2800
514	blk.29.post_norm.weight	0x102795ba0	0x2800
515	blk.29.proj.weight	0x1027983a0	0x140000
516	blk.30.attn_k.weight	0x1028d83a0	0x106800
517	blk.30.attn_k_norm.weight	0x1029deba0	0x400
518	blk.30.attn_norm.weight	0x1029defa0	0x2800
519	blk.30.attn_output.weight	0x1029e17a0	0x41a000
520	blk.30.attn_q.weight	0x102dfb7a0	0x370000
521	blk.30.attn_q_norm.weight	0x10316b7a0	0x400
522	blk.30.attn_v.weight	0x10316bba0	0x106800
523	blk.30.ffn_down.weight	0x1032723a0	0x1130000
524	blk.30.ffn_gate.weight	0x1043a23a0	0x1130000
525	blk.30.ffn_norm.weight	0x1054d23a0	0x2800
526	blk.30.ffn_up.weight	0x1054d4ba0	0x1482000
527	blk.30.inp_gate.weight	0x106956ba0	0x83400
528	blk.30.layer_output_scale.weight	0x1069d9fa0	0x4
529	blk.30.post_attention_norm.weight	0x1069d9fc0	0x2800
530	blk.30.post_ffw_norm.weight	0x1069dc7c0	0x2800
531	blk.30.post_norm.weight	0x1069defc0	0x2800
532	blk.30.proj.weight	0x1069e17c0	0x140000
533	blk.31.attn_k.weight	0x106b217c0	0x106800
534	blk.31.attn_k_norm.weight	0x106c27fc0	0x400
535	blk.31.attn_norm.weight	0x106c283c0	0x2800
536	blk.31.attn_output.weight	0x106c2abc0	0x41a000
537	blk.31.attn_q.weight	0x107044bc0	0x370000
538	blk.31.attn_q_norm.weight	0x1073b4bc0	0x400
539	blk.31.attn_v.weight	0x1073b4fc0	0x106800
540	blk.31.ffn_down.weight	0x1074bb7c0	0x1130000
541	blk.31.ffn_gate.weight	0x1085eb7c0	0x1130000
542	blk.31.ffn_norm.weight	0x10971b7c0	0x2800
543	blk.31.ffn_up.weight	0x10971dfc0	0x1482000
544	blk.31.inp_gate.weight	0x10ab9ffc0	0x83400
545	blk.31.layer_output_scale.weight	0x10ac233c0	0x4
546	blk.31.post_attention_norm.weight	0x10ac233e0	0x2800
547	blk.31.post_ffw_norm.weight	0x10ac25be0	0x2800
548	blk.31.post_norm.weight	0x10ac283e0	0x2800
549	blk.31.proj.weight	0x10ac2abe0	0x140000
550	blk.32.attn_k.weight	0x10ad6abe0	0x106800
551	blk.32.attn_k_norm.weight	0x10ae713e0	0x400
552	blk.32.attn_norm.weight	0x10ae717e0	0x2800
553	blk.32.attn_output.weight	0x10ae73fe0	0x41a000
554	blk.32.attn_q.weight	0x10b28dfe0	0x370000
555	blk.32.attn_q_norm.weight	0x10b5fdfe0	0x400
556	blk.32.attn_v.weight	0x10b5fe3e0	0x106800
557	blk.32.ffn_down.weight	0x10b704be0	0x1130000
558	blk.32.ffn_gate.weight	0x10c834be0	0x1130000
559	blk.32.ffn_norm.weight	0x10d964be0	0x2800
560	blk.32.ffn_up.weight	0x10d9673e0	0x1482000
561	blk.32.inp_gate.weight	0x10ede93e0	0x83400
562	blk.32.layer_output_scale.weight	0x10ee6c7e0	0x4
563	blk.32.post_attention_norm.weight	0x10ee6c800	0x2800
564	blk.32.post_ffw_norm.weight	0x10ee6f000	0x2800
565	blk.32.post_norm.weight	0x10ee71800	0x2800
566	blk.32.proj.weight	0x10ee74000	0x78000
567	blk.33.attn_k.weight	0x10eeec000	0x106800
568	blk.33.attn_k_norm.weight	0x10eff2800	0x400
569	blk.33.attn_norm.weight	0x10eff2c00	0x2800
570	blk.33.attn_output.weight	0x10eff5400	0x41a000
571	blk.33.attn_q.weight	0x10f40f400	0x370000
572	blk.33.attn_q_norm.weight	0x10f77f400	0x400
573	blk.33.attn_v.weight	0x10f77f800	0x106800
574	blk.33.ffn_down.weight	0x10f886000	0x1130000
575	blk.33.ffn_gate.weight	0x1109b6000	0x1130000
576	blk.33.ffn_norm.weight	0x111ae6000	0x2800
577	blk.33.ffn_up.weight	0x111ae8800	0x1482000
578	blk.33.inp_gate.weight	0x112f6a800	0x6e000
579	blk.33.layer_output_scale.weight	0x112fd8800	0x4
580	blk.33.post_attention_norm.weight	0x112fd8820	0x2800
581	blk.33.post_ffw_norm.weight	0x112fdb020	0x2800
582	blk.33.post_norm.weight	0x112fdd820	0x2800
583	blk.33.proj.weight	0x112fe0020	0x78000
584	blk.34.attn_k.weight	0x113058020	0x106800
585	blk.34.attn_k_norm.weight	0x11315e820	0x400
586	blk.34.attn_norm.weight	0x11315ec20	0x2800
587	blk.34.attn_output.weight	0x113161420	0x41a000
588	blk.34.attn_q.weight	0x11357b420	0x370000
589	blk.34.attn_q_norm.weight	0x1138eb420	0x400
590	blk.34.attn_v.weight	0x1138eb820	0x106800
591	blk.34.ffn_down.weight	0x1139f2020	0x1130000
592	blk.34.ffn_gate.weight	0x114b22020	0x1130000
593	blk.34.ffn_norm.weight	0x115c52020	0x2800
594	blk.34.ffn_up.weight	0x115c54820	0x1482000
595	blk.34.inp_gate.weight	0x1170d6820	0x83400
596	blk.34.layer_output_scale.weight	0x117159c20	0x4
597	blk.34.post_attention_norm.weight	0x117159c40	0x2800
598	blk.34.post_ffw_norm.weight	0x11715c440	0x2800
599	blk.34.post_norm.weight	0x11715ec40	0x2800
600	blk.34.proj.weight	0x117161440	0x78000
601	blk.35.attn_k.weight	0x1171d9440	0x20d000
602	blk.35.attn_k_norm.weight	0x1173e6440	0x800
603	blk.35.attn_norm.weight	0x1173e6c40	0x2800
604	blk.35.attn_output.weight	0x1173e9440	0x6e0000
605	blk.35.attn_q.weight	0x117ac9440	0x6e0000
606	blk.35.attn_q_norm.weight	0x1181a9440	0x800
607	blk.35.attn_v.weight	0x1181a9c40	0x20d000
608	blk.35.ffn_down.weight	0x1183b6c40	0x1130000
609	blk.35.ffn_gate.weight	0x1194e6c40	0x1130000
610	blk.35.ffn_norm.weight	0x11a616c40	0x2800
611	blk.35.ffn_up.weight	0x11a619440	0x1482000
612	blk.35.inp_gate.weight	0x11ba9b440	0x6e000
613	blk.35.layer_output_scale.weight	0x11bb09440	0x4
614	blk.35.post_attention_norm.weight	0x11bb09460	0x2800
615	blk.35.post_ffw_norm.weight	0x11bb0bc60	0x2800
616	blk.35.post_norm.weight	0x11bb0e460	0x2800
617	blk.35.proj.weight	0x11bb10c60	0x78000
618	blk.36.attn_k.weight	0x11bb88c60	0x106800
619	blk.36.attn_k_norm.weight	0x11bc8f460	0x400
620	blk.36.attn_norm.weight	0x11bc8f860	0x2800
621	blk.36.attn_output.weight	0x11bc92060	0x41a000
622	blk.36.attn_q.weight	0x11c0ac060	0x370000
623	blk.36.attn_q_norm.weight	0x11c41c060	0x400
624	blk.36.attn_v.weight	0x11c41c460	0x106800
625	blk.36.ffn_down.weight	0x11c522c60	0x1130000
626	blk.36.ffn_gate.weight	0x11d652c60	0x1130000
627	blk.36.ffn_norm.weight	0x11e782c60	0x2800
628	blk.36.ffn_up.weight	0x11e785460	0x1482000
629	blk.36.inp_gate.weight	0x11fc07460	0x6e000
630	blk.36.layer_output_scale.weight	0x11fc75460	0x4
631	blk.36.post_attention_norm.weight	0x11fc75480	0x2800
632	blk.36.post_ffw_norm.weight	0x11fc77c80	0x2800
633	blk.36.post_norm.weight	0x11fc7a480	0x2800
634	blk.36.proj.weight	0x11fc7cc80	0x6e000
635	blk.37.attn_k.weight	0x11fceac80	0x106800
636	blk.37.attn_k_norm.weight	0x11fdf1480	0x400
637	blk.37.attn_norm.weight	0x11fdf1880	0x2800
638	blk.37.attn_output.weight	0x11fdf4080	0x41a000
639	blk.37.attn_q.weight	0x12020e080	0x370000
640	blk.37.attn_q_norm.weight	0x12057e080	0x400
641	blk.37.attn_v.weight	0x12057e480	0x106800
642	blk.37.ffn_down.weight	0x120684c80	0x1130000
643	blk.37.ffn_gate.weight	0x1217b4c80	0x1130000
644	blk.37.ffn_norm.weight	0x1228e4c80	0x2800
645	blk.37.ffn_up.weight	0x1228e7480	0x1482000
646	blk.37.inp_gate.weight	0x123d69480	0x6e000
647	blk.37.layer_output_scale.weight	0x123dd7480	0x4
648	blk.37.post_attention_norm.weight	0x123dd74a0	0x2800
649	blk.37.post_ffw_norm.weight	0x123dd9ca0	0x2800
650	blk.37.post_norm.weight	0x123ddc4a0	0x2800
651	blk.37.proj.weight	0x123ddeca0	0x78000
652	blk.38.attn_k.weight	0x123e56ca0	0x106800
653	blk.38.attn_k_norm.weight	0x123f5d4a0	0x400
654	blk.38.attn_norm.weight	0x123f5d8a0	0x2800
655	blk.38.attn_output.weight	0x123f600a0	0x41a000
656	blk.38.attn_q.weight	0x12437a0a0	0x370000
657	blk.38.attn_q_norm.weight	0x1246ea0a0	0x400
658	blk.38.attn_v.weight	0x1246ea4a0	0x106800
659	blk.38.ffn_down.weight	0x1247f0ca0	0x1130000
660	blk.38.ffn_gate.weight	0x125920ca0	0x1130000
661	blk.38.ffn_norm.weight	0x126a50ca0	0x2800
662	blk.38.ffn_up.weight	0x126a534a0	0x1482000
663	blk.38.inp_gate.weight	0x127ed54a0	0x6e000
664	blk.38.layer_output_scale.weight	0x127f434a0	0x4
665	blk.38.post_attention_norm.weight	0x127f434c0	0x2800
666	blk.38.post_ffw_norm.weight	0x127f45cc0	0x2800
667	blk.38.post_norm.weight	0x127f484c0	0x2800
668	blk.38.proj.weight	0x127f4acc0	0x140000
669	blk.39.attn_k.weight	0x12808acc0	0x106800
670	blk.39.attn_k_norm.weight	0x1281914c0	0x400
671	blk.39.attn_norm.weight	0x1281918c0	0x2800
672	blk.39.attn_output.weight	0x1281940c0	0x41a000
673	blk.39.attn_q.weight	0x1285ae0c0	0x370000
674	blk.39.attn_q_norm.weight	0x12891e0c0	0x400
675	blk.39.attn_v.weight	0x12891e4c0	0x106800
676	blk.39.ffn_down.weight	0x128a24cc0	0x1130000
677	blk.39.ffn_gate.weight	0x129b54cc0	0x1130000
678	blk.39.ffn_norm.weight	0x12ac84cc0	0x2800
679	blk.39.ffn_up.weight	0x12ac874c0	0x1482000
680	blk.39.inp_gate.weight	0x12c1094c0	0x6e000
681	blk.39.layer_output_scale.weight	0x12c1774c0	0x4
682	blk.39.post_attention_norm.weight	0x12c1774e0	0x2800
683	blk.39.post_ffw_norm.weight	0x12c179ce0	0x2800
684	blk.39.post_norm.weight	0x12c17c4e0	0x2800
685	blk.39.proj.weight	0x12c17ece0	0x78000
686	blk.40.attn_k.weight	0x12c1f6ce0	0x106800
687	blk.40.attn_k_norm.weight	0x12c2fd4e0	0x400
688	blk.40.attn_norm.weight	0x12c2fd8e0	0x2800
689	blk.40.attn_output.weight	0x12c3000e0	0x41a000
690	blk.40.attn_q.weight	0x12c71a0e0	0x370000
691	blk.40.attn_q_norm.weight	0x12ca8a0e0	0x400
692	blk.40.attn_v.weight	0x12ca8a4e0	0x106800
693	blk.40.ffn_down.weight	0x12cb90ce0	0x1130000
694	blk.40.ffn_gate.weight	0x12dcc0ce0	0x1482000
695	blk.40.ffn_norm.weight	0x12f142ce0	0x2800
696	blk.40.ffn_up.weight	0x12f1454e0	0x1482000
697	blk.40.inp_gate.weight	0x1305c74e0	0x6e000
698	blk.40.layer_output_scale.weight	0x1306354e0	0x4
699	blk.40.post_attention_norm.weight	0x130635500	0x2800
700	blk.40.post_ffw_norm.weight	0x130637d00	0x2800
701	blk.40.post_norm.weight	0x13063a500	0x2800
702	blk.40.proj.weight	0x13063cd00	0x78000
703	blk.41.attn_k.weight	0x1306b4d00	0x20d000
704	blk.41.attn_k_norm.weight	0x1308c1d00	0x800
705	blk.41.attn_norm.weight	0x1308c2500	0x2800
706	blk.41.attn_output.weight	0x1308c4d00	0x6e0000
707	blk.41.attn_q.weight	0x130fa4d00	0x6e0000
708	blk.41.attn_q_norm.weight	0x131684d00	0x800
709	blk.41.attn_v.weight	0x131685500	0x20d000
710	blk.41.ffn_down.weight	0x131892500	0x1130000
711	blk.41.ffn_gate.weight	0x1329c2500	0x1130000
712	blk.41.ffn_norm.weight	0x133af2500	0x2800
713	blk.41.ffn_up.weight	0x133af4d00	0x1482000
714	blk.41.inp_gate.weight	0x134f76d00	0x5a000
715	blk.41.layer_output_scale.weight	0x134fd0d00	0x4
716	blk.41.post_attention_norm.weight	0x134fd0d20	0x2800
717	blk.41.post_ffw_norm.weight	0x134fd3520	0x2800
718	blk.41.post_norm.weight	0x134fd5d20	0x2800
719	blk.41.proj.weight	0x134fd8520	0x78000

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	Q4_K	4.5000
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	Q6_K	6.5625

Total elements in base: ( ~4B) 3517189120
Percentage of total elements: 46.78%
Bits per Weight (BPW) for base: 4.9836 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	Q5_K	5.5000
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	Q5_K	5.5000
10	blk.0.attn_q.weight	Block 0 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
13	blk.0.ffn_down.weight	Block 0 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q4_K	4.5000
14	blk.0.ffn_gate.weight	Block 0 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
17	blk.0.inp_gate.weight	Block 0 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_1	6.0000
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	Q8_0	8.5000

Total elements in blk.0: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.0: 5.5610 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	Q5_K	5.5000
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	Q6_K	6.5625
27	blk.1.attn_q.weight	Block 1 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
30	blk.1.ffn_down.weight	Block 1 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
31	blk.1.ffn_gate.weight	Block 1 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
34	blk.1.inp_gate.weight	Block 1 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_1	6.0000
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	Q8_0	8.5000

Total elements in blk.1: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.1: 5.9025 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	Q5_K	5.5000
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	Q6_K	6.5625
44	blk.2.attn_q.weight	Block 2 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
47	blk.2.ffn_down.weight	Block 2 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
48	blk.2.ffn_gate.weight	Block 2 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
51	blk.2.inp_gate.weight	Block 2 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_1	6.0000
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	Q8_0	8.5000

Total elements in blk.2: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.2: 5.9025 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	Q5_K	5.5000
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	Q6_K	6.5625
61	blk.3.attn_q.weight	Block 3 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
64	blk.3.ffn_down.weight	Block 3 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
65	blk.3.ffn_gate.weight	Block 3 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
68	blk.3.inp_gate.weight	Block 3 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625

Total elements in blk.3: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.3: 5.8853 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	Q5_K	5.5000
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	Q6_K	6.5625
78	blk.4.attn_q.weight	Block 4 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
81	blk.4.ffn_down.weight	Block 4 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
82	blk.4.ffn_gate.weight	Block 4 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
85	blk.4.inp_gate.weight	Block 4 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.4: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.4: 5.8814 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	Q5_K	5.5000
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	Q5_K	5.5000
95	blk.5.attn_q.weight	Block 5 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
98	blk.5.ffn_down.weight	Block 5 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
99	blk.5.ffn_gate.weight	Block 5 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
102	blk.5.inp_gate.weight	Block 5 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_1	6.0000
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	Q5_1	6.0000

Total elements in blk.5: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.5: 5.7982 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	Q5_K	5.5000
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	Q6_K	6.5625
112	blk.6.attn_q.weight	Block 6 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
115	blk.6.ffn_down.weight	Block 6 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
116	blk.6.ffn_gate.weight	Block 6 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
119	blk.6.inp_gate.weight	Block 6 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_1	6.0000
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	Q5_1	6.0000

Total elements in blk.6: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.6: 5.8849 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	Q5_K	5.5000
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	Q6_K	6.5625
129	blk.7.attn_q.weight	Block 7 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
132	blk.7.ffn_down.weight	Block 7 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
133	blk.7.ffn_gate.weight	Block 7 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
136	blk.7.inp_gate.weight	Block 7 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.7: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.7: 5.8889 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	Q5_K	5.5000
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	Q6_K	6.5625
146	blk.8.attn_q.weight	Block 8 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
149	blk.8.ffn_down.weight	Block 8 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
150	blk.8.ffn_gate.weight	Block 8 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
153	blk.8.inp_gate.weight	Block 8 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.8: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.8: 5.8889 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	Q5_K	5.5000
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	Q6_K	6.5625
163	blk.9.attn_q.weight	Block 9 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
166	blk.9.ffn_down.weight	Block 9 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
167	blk.9.ffn_gate.weight	Block 9 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
170	blk.9.inp_gate.weight	Block 9 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.9: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.9: 5.8889 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	Q5_K	5.5000
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	Q6_K	6.5625
180	blk.10.attn_q.weight	Block 10 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
183	blk.10.ffn_down.weight	Block 10 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
184	blk.10.ffn_gate.weight	Block 10 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
187	blk.10.inp_gate.weight	Block 10 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	F16	16.0000

Total elements in blk.10: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.10: 6.2585 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	Q5_K	5.5000
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	Q5_K	5.5000
197	blk.11.attn_q.weight	Block 11 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
200	blk.11.ffn_down.weight	Block 11 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
201	blk.11.ffn_gate.weight	Block 11 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
204	blk.11.inp_gate.weight	Block 11 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.11: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.11: 5.8016 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	Q5_K	5.5000
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	Q6_K	6.5625
214	blk.12.attn_q.weight	Block 12 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
217	blk.12.ffn_down.weight	Block 12 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
218	blk.12.ffn_gate.weight	Block 12 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
221	blk.12.inp_gate.weight	Block 12 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.12: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.12: 5.8889 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	Q5_K	5.5000
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	Q6_K	6.5625
231	blk.13.attn_q.weight	Block 13 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
234	blk.13.ffn_down.weight	Block 13 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
235	blk.13.ffn_gate.weight	Block 13 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
238	blk.13.inp_gate.weight	Block 13 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.13: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.13: 5.9518 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	Q5_K	5.5000
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	Q6_K	6.5625
248	blk.14.attn_q.weight	Block 14 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
251	blk.14.ffn_down.weight	Block 14 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
252	blk.14.ffn_gate.weight	Block 14 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
255	blk.14.inp_gate.weight	Block 14 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	F16	16.0000

Total elements in blk.14: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.14: 5.9593 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	Q5_K	5.5000
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	Q6_K	6.5625
265	blk.15.attn_q.weight	Block 15 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
268	blk.15.ffn_down.weight	Block 15 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
269	blk.15.ffn_gate.weight	Block 15 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
272	blk.15.inp_gate.weight	Block 15 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.15: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.15: 6.2510 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	Q5_K	5.5000
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	Q6_K	6.5625
282	blk.16.attn_q.weight	Block 16 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
285	blk.16.ffn_down.weight	Block 16 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
286	blk.16.ffn_gate.weight	Block 16 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
289	blk.16.inp_gate.weight	Block 16 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.16: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.16: 6.1806 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	Q5_K	5.5000
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	Q5_K	5.5000
299	blk.17.attn_q.weight	Block 17 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
302	blk.17.ffn_down.weight	Block 17 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
303	blk.17.ffn_gate.weight	Block 17 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
306	blk.17.inp_gate.weight	Block 17 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.17: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.17: 6.0639 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	Q6_K	6.5625
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	Q6_K	6.5625
316	blk.18.attn_q.weight	Block 18 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
319	blk.18.ffn_down.weight	Block 18 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
320	blk.18.ffn_gate.weight	Block 18 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
323	blk.18.inp_gate.weight	Block 18 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q8_0	8.5000
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	F16	16.0000

Total elements in blk.18: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.18: 5.9879 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	Q6_K	6.5625
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	Q6_K	6.5625
333	blk.19.attn_q.weight	Block 19 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
336	blk.19.ffn_down.weight	Block 19 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
337	blk.19.ffn_gate.weight	Block 19 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
340	blk.19.inp_gate.weight	Block 19 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q8_0	8.5000

Total elements in blk.19: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.19: 5.9214 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	Q6_K	6.5625
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	Q6_K	6.5625
350	blk.20.attn_q.weight	Block 20 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
353	blk.20.ffn_down.weight	Block 20 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
354	blk.20.ffn_gate.weight	Block 20 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
357	blk.20.inp_gate.weight	Block 20 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.20: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.20: 6.2031 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	Q6_K	6.5625
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	Q6_K	6.5625
367	blk.21.attn_q.weight	Block 21 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
370	blk.21.ffn_down.weight	Block 21 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
371	blk.21.ffn_gate.weight	Block 21 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
374	blk.21.inp_gate.weight	Block 21 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q8_0	8.5000

Total elements in blk.21: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.21: 6.2805 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	Q6_K	6.5625
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	Q6_K	6.5625
384	blk.22.attn_q.weight	Block 22 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
387	blk.22.ffn_down.weight	Block 22 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
388	blk.22.ffn_gate.weight	Block 22 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
391	blk.22.inp_gate.weight	Block 22 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.22: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.22: 5.9562 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	Q6_K	6.5625
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	Q5_K	5.5000
401	blk.23.attn_q.weight	Block 23 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
404	blk.23.ffn_down.weight	Block 23 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
405	blk.23.ffn_gate.weight	Block 23 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
408	blk.23.inp_gate.weight	Block 23 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.23: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.23: 6.1951 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	Q6_K	6.5625
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	Q6_K	6.5625
418	blk.24.attn_q.weight	Block 24 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
421	blk.24.ffn_down.weight	Block 24 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
422	blk.24.ffn_gate.weight	Block 24 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
425	blk.24.inp_gate.weight	Block 24 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.24: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.24: 6.2031 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	Q6_K	6.5625
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	Q6_K	6.5625
435	blk.25.attn_q.weight	Block 25 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
438	blk.25.ffn_down.weight	Block 25 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
439	blk.25.ffn_gate.weight	Block 25 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
442	blk.25.inp_gate.weight	Block 25 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.25: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.25: 5.8963 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	Q6_K	6.5625
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	Q6_K	6.5625
452	blk.26.attn_q.weight	Block 26 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
455	blk.26.ffn_down.weight	Block 26 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
456	blk.26.ffn_gate.weight	Block 26 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q5_K	5.5000
459	blk.26.inp_gate.weight	Block 26 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.26: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.26: 5.6675 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	Q6_K	6.5625
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	Q6_K	6.5625
469	blk.27.attn_q.weight	Block 27 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
472	blk.27.ffn_down.weight	Block 27 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
473	blk.27.ffn_gate.weight	Block 27 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
476	blk.27.inp_gate.weight	Block 27 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.27: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.27: 5.9668 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	Q6_K	6.5625
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	Q6_K	6.5625
486	blk.28.attn_q.weight	Block 28 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
489	blk.28.ffn_down.weight	Block 28 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
490	blk.28.ffn_gate.weight	Block 28 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
493	blk.28.inp_gate.weight	Block 28 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.28: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.28: 5.9668 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	Q6_K	6.5625
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	Q5_K	5.5000
503	blk.29.attn_q.weight	Block 29 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
506	blk.29.ffn_down.weight	Block 29 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
507	blk.29.ffn_gate.weight	Block 29 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
510	blk.29.inp_gate.weight	Block 29 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	F16	16.0000

Total elements in blk.29: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.29: 5.8896 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	Q6_K	6.5625
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	Q6_K	6.5625
520	blk.30.attn_q.weight	Block 30 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
523	blk.30.ffn_down.weight	Block 30 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
524	blk.30.ffn_gate.weight	Block 30 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
527	blk.30.inp_gate.weight	Block 30 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	F16	16.0000

Total elements in blk.30: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.30: 5.9742 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	Q6_K	6.5625
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	Q6_K	6.5625
537	blk.31.attn_q.weight	Block 31 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
540	blk.31.ffn_down.weight	Block 31 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
541	blk.31.ffn_gate.weight	Block 31 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
544	blk.31.inp_gate.weight	Block 31 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	F16	16.0000

Total elements in blk.31: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.31: 5.9742 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	Q6_K	6.5625
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	Q6_K	6.5625
554	blk.32.attn_q.weight	Block 32 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
557	blk.32.ffn_down.weight	Block 32 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
558	blk.32.ffn_gate.weight	Block 32 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
561	blk.32.inp_gate.weight	Block 32 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.32: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.32: 5.9038 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	Q6_K	6.5625
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	Q6_K	6.5625
571	blk.33.attn_q.weight	Block 33 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
574	blk.33.ffn_down.weight	Block 33 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
575	blk.33.ffn_gate.weight	Block 33 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
578	blk.33.inp_gate.weight	Block 33 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.33: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.33: 5.8963 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	Q6_K	6.5625
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	Q6_K	6.5625
588	blk.34.attn_q.weight	Block 34 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
591	blk.34.ffn_down.weight	Block 34 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
592	blk.34.ffn_gate.weight	Block 34 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
595	blk.34.inp_gate.weight	Block 34 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q6_K	6.5625
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	Q5_1	6.0000

Total elements in blk.34: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.34: 5.9038 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	Q6_K	6.5625
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	Q5_K	5.5000
605	blk.35.attn_q.weight	Block 35 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
608	blk.35.ffn_down.weight	Block 35 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
609	blk.35.ffn_gate.weight	Block 35 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
612	blk.35.inp_gate.weight	Block 35 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.35: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.35: 5.8213 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	Q6_K	6.5625
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	Q6_K	6.5625
622	blk.36.attn_q.weight	Block 36 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
625	blk.36.ffn_down.weight	Block 36 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
626	blk.36.ffn_gate.weight	Block 36 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
629	blk.36.inp_gate.weight	Block 36 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_K	5.5000

Total elements in blk.36: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.36: 5.8928 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	Q6_K	6.5625
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	Q6_K	6.5625
639	blk.37.attn_q.weight	Block 37 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
642	blk.37.ffn_down.weight	Block 37 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
643	blk.37.ffn_gate.weight	Block 37 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
646	blk.37.inp_gate.weight	Block 37 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.37: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.37: 5.8963 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	Q6_K	6.5625
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	Q6_K	6.5625
656	blk.38.attn_q.weight	Block 38 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
659	blk.38.ffn_down.weight	Block 38 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
660	blk.38.ffn_gate.weight	Block 38 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
663	blk.38.inp_gate.weight	Block 38 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	F16	16.0000

Total elements in blk.38: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.38: 5.9668 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	Q6_K	6.5625
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	Q6_K	6.5625
673	blk.39.attn_q.weight	Block 39 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
676	blk.39.ffn_down.weight	Block 39 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
677	blk.39.ffn_gate.weight	Block 39 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
680	blk.39.inp_gate.weight	Block 39 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.39: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.39: 5.8963 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	Q6_K	6.5625
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	Q6_K	6.5625
690	blk.40.attn_q.weight	Block 40 Attention Query (W)	( ~5M) 5242880	2560 x 2048 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
693	blk.40.ffn_down.weight	Block 40 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
694	blk.40.ffn_gate.weight	Block 40 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q6_K	6.5625
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	Q6_K	6.5625
697	blk.40.inp_gate.weight	Block 40 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q5_K	5.5000
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	Q5_1	6.0000

Total elements in blk.40: (~93M) 93074433
Percentage of total elements: 1.24%
Bits per Weight (BPW) for blk.40: 6.1956 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	Q6_K	6.5625
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	Q5_K	5.5000
707	blk.41.attn_q.weight	Block 41 Attention Query (W)	( ~10M) 10485760	2560 x 4096 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
710	blk.41.ffn_down.weight	Block 41 Feed-Forward Network "Down" (W)	( ~26M) 26214400	10240 x 2560 x 1 x 1	Q5_K	5.5000
711	blk.41.ffn_gate.weight	Block 41 Feed-Forward Network "Gate" (W)	( ~26M) 26214400	2560 x 10240 x 1 x 1	Q5_K	5.5000
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	Q6_K	6.5625
714	blk.41.inp_gate.weight	Block 41 Inp_Gate (W)	(~655K) 655360	2560 x 256 x 1 x 1	Q4_K	4.5000
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	Q5_1	6.0000

Total elements in blk.41: (~106M) 106182145
Percentage of total elements: 1.41%
Bits per Weight (BPW) for blk.41: 5.8151 bits

Total BPW for gemma-4-E4B-it-Q5_K.gguf: 5.5000 bits