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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

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

Install from WinGet (Windows)

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

Use pre-built binary

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

Build from source code

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

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

eaddario

Add GGUF internal file structure

66279ab verified over 1 year ago

preview code

Raw

History Blame

83.1 kB

DeepSeek-R1-Distill-Llama-8B-Q3_K_L.gguf - GGUF Internal File Dump

Endian: LITTLE endian

Key Value Metadata Store

There are 36 key-value pairs in this file

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

Tensors Overview ~8B Elements

Total number of elements in all tensors: 8030261312 Elements

DeepSeek-R1-Distill-Llama-8B-Q3_K_L.gguf - GGUF Internal File Dump
- Key Value Metadata Store
- Tensors Overview ~8B Elements

Tensor Data Offset

This table contains the offset and data segment relative to start of file

T_ID	Tensor Layer Name	Data Offset (B)	Data Size (B)
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10	blk.0.ffn_gate.weight	0x1f265b80	0x1810000
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12	blk.0.ffn_up.weight	0x20a79b80	0x1810000
13	blk.1.attn_k.weight	0x22289b80	0x1b8000
14	blk.1.attn_norm.weight	0x22441b80	0x4000
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210	blk.22.ffn_up.weight	0xad451b80	0x1260000
211	blk.23.attn_k.weight	0xae6b1b80	0x150000
212	blk.23.attn_norm.weight	0xae801b80	0x4000
213	blk.23.attn_output.weight	0xae805b80	0xb00000
214	blk.23.attn_q.weight	0xaf305b80	0x540000
215	blk.23.attn_v.weight	0xaf845b80	0x240000
216	blk.23.ffn_down.weight	0xafa85b80	0x1f80000
217	blk.23.ffn_gate.weight	0xb1a05b80	0x1260000
218	blk.23.ffn_norm.weight	0xb2c65b80	0x4000
219	blk.23.ffn_up.weight	0xb2c69b80	0x1260000
220	blk.24.attn_k.weight	0xb3ec9b80	0x150000
221	blk.24.attn_norm.weight	0xb4019b80	0x4000
222	blk.24.attn_output.weight	0xb401db80	0xb00000
223	blk.24.attn_q.weight	0xb4b1db80	0x540000
224	blk.24.attn_v.weight	0xb505db80	0x240000
225	blk.24.ffn_down.weight	0xb529db80	0x1f80000
226	blk.24.ffn_gate.weight	0xb721db80	0x1260000
227	blk.24.ffn_norm.weight	0xb847db80	0x4000
228	blk.24.ffn_up.weight	0xb8481b80	0x1260000
229	blk.25.attn_k.weight	0xb96e1b80	0x150000
230	blk.25.attn_norm.weight	0xb9831b80	0x4000
231	blk.25.attn_output.weight	0xb9835b80	0xb00000
232	blk.25.attn_q.weight	0xba335b80	0x540000
233	blk.25.attn_v.weight	0xba875b80	0x240000
234	blk.25.ffn_down.weight	0xbaab5b80	0x1f80000
235	blk.25.ffn_gate.weight	0xbca35b80	0x1260000
236	blk.25.ffn_norm.weight	0xbdc95b80	0x4000
237	blk.25.ffn_up.weight	0xbdc99b80	0x1260000
238	blk.26.attn_k.weight	0xbeef9b80	0x150000
239	blk.26.attn_norm.weight	0xbf049b80	0x4000
240	blk.26.attn_output.weight	0xbf04db80	0xb00000
241	blk.26.attn_q.weight	0xbfb4db80	0x540000
242	blk.26.attn_v.weight	0xc008db80	0x240000
243	blk.26.ffn_down.weight	0xc02cdb80	0x1f80000
244	blk.26.ffn_gate.weight	0xc224db80	0x1260000
245	blk.26.ffn_norm.weight	0xc34adb80	0x4000
246	blk.26.ffn_up.weight	0xc34b1b80	0x1260000
247	blk.27.attn_k.weight	0xc4711b80	0x150000
248	blk.27.attn_norm.weight	0xc4861b80	0x4000
249	blk.27.attn_output.weight	0xc4865b80	0xb00000
250	blk.27.attn_q.weight	0xc5365b80	0x540000
251	blk.27.attn_v.weight	0xc58a5b80	0x240000
252	blk.27.ffn_down.weight	0xc5ae5b80	0x1f80000
253	blk.27.ffn_gate.weight	0xc7a65b80	0x1260000
254	blk.27.ffn_norm.weight	0xc8cc5b80	0x4000
255	blk.27.ffn_up.weight	0xc8cc9b80	0x1260000
256	blk.28.attn_k.weight	0xc9f29b80	0x150000
257	blk.28.attn_norm.weight	0xca079b80	0x4000
258	blk.28.attn_output.weight	0xca07db80	0xb00000
259	blk.28.attn_q.weight	0xcab7db80	0x540000
260	blk.28.attn_v.weight	0xcb0bdb80	0x240000
261	blk.28.ffn_down.weight	0xcb2fdb80	0x1f80000
262	blk.28.ffn_gate.weight	0xcd27db80	0x1260000
263	blk.28.ffn_norm.weight	0xce4ddb80	0x4000
264	blk.28.ffn_up.weight	0xce4e1b80	0x1260000
265	blk.29.attn_k.weight	0xcf741b80	0x150000
266	blk.29.attn_norm.weight	0xcf891b80	0x4000
267	blk.29.attn_output.weight	0xcf895b80	0xb00000
268	blk.29.attn_q.weight	0xd0395b80	0x540000
269	blk.29.attn_v.weight	0xd08d5b80	0x240000
270	blk.29.ffn_down.weight	0xd0b15b80	0x1f80000
271	blk.29.ffn_gate.weight	0xd2a95b80	0x1260000
272	blk.29.ffn_norm.weight	0xd3cf5b80	0x4000
273	blk.29.ffn_up.weight	0xd3cf9b80	0x1260000
274	blk.30.attn_k.weight	0xd4f59b80	0x150000
275	blk.30.attn_norm.weight	0xd50a9b80	0x4000
276	blk.30.attn_output.weight	0xd50adb80	0xb00000
277	blk.30.attn_q.weight	0xd5badb80	0x540000
278	blk.30.attn_v.weight	0xd60edb80	0x240000
279	blk.30.ffn_down.weight	0xd632db80	0x1f80000
280	blk.30.ffn_gate.weight	0xd82adb80	0x1260000
281	blk.30.ffn_norm.weight	0xd950db80	0x4000
282	blk.30.ffn_up.weight	0xd9511b80	0x1260000
283	blk.31.attn_k.weight	0xda771b80	0x150000
284	blk.31.attn_norm.weight	0xda8c1b80	0x4000
285	blk.31.attn_output.weight	0xda8c5b80	0xb00000
286	blk.31.attn_q.weight	0xdb3c5b80	0x540000
287	blk.31.attn_v.weight	0xdb905b80	0x240000
288	blk.31.ffn_down.weight	0xdbb45b80	0x1f80000
289	blk.31.ffn_gate.weight	0xddac5b80	0x1260000
290	blk.31.ffn_norm.weight	0xded25b80	0x4000
291	blk.31.ffn_up.weight	0xded29b80	0x1260000

Base Tensor Group : ~1B Elements

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

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

Block 0 Tensor Group : ~218M Elements

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

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

Block 1 Tensor Group : ~218M Elements

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

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

Block 2 Tensor Group : ~218M Elements

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

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

Block 3 Tensor Group : ~218M Elements

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

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

Block 4 Tensor Group : ~218M Elements

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

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

Block 5 Tensor Group : ~218M Elements

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

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

Block 6 Tensor Group : ~218M Elements

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

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

Block 7 Tensor Group : ~218M Elements

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

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

Block 8 Tensor Group : ~218M Elements

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

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

Block 9 Tensor Group : ~218M Elements

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

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

Block 10 Tensor Group : ~218M Elements

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

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

Block 11 Tensor Group : ~218M Elements

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

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

Block 12 Tensor Group : ~218M Elements

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

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

Block 13 Tensor Group : ~218M Elements

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

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

Block 14 Tensor Group : ~218M Elements

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

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

Block 15 Tensor Group : ~218M Elements

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

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

Block 16 Tensor Group : ~218M Elements

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

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

Block 17 Tensor Group : ~218M Elements

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

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

Block 18 Tensor Group : ~218M Elements

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

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

Block 19 Tensor Group : ~218M Elements

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

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

Block 20 Tensor Group : ~218M Elements

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

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

Block 21 Tensor Group : ~218M Elements

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

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

Block 22 Tensor Group : ~218M Elements

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

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

Block 23 Tensor Group : ~218M Elements

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

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

Block 24 Tensor Group : ~218M Elements

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

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

Block 25 Tensor Group : ~218M Elements

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

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

Block 26 Tensor Group : ~218M Elements

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

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

Block 27 Tensor Group : ~218M Elements

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

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

Block 28 Tensor Group : ~218M Elements

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

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

Block 29 Tensor Group : ~218M Elements

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

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

Block 30 Tensor Group : ~218M Elements

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

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

Block 31 Tensor Group : ~218M Elements

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

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