Run 7B model locally — Part 3 – Page 2

Darren2025-09-18T14:55:30-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 16384 -b 64 -t $(nproc) -no-cnv -n 128 -p "test"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11641 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 339 tensors from models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen2
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen2.5 7B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen2.5
llama_model_loader: – kv 5: general.size_label str = 7B
llama_model_loader: – kv 6: general.license str = apache-2.0
llama_model_loader: – kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7…
llama_model_loader: – kv 8: general.base_model.count u32 = 1
llama_model_loader: – kv 9: general.base_model.0.name str = Qwen2.5 7B
llama_model_loader: – kv 10: general.base_model.0.organization str = Qwen
llama_model_loader: – kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-7B
llama_model_loader: – kv 12: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: – kv 13: general.languages arr[str,1] = ["en"]
llama_model_loader: – kv 14: qwen2.block_count u32 = 28
llama_model_loader: – kv 15: qwen2.context_length u32 = 32768
llama_model_loader: – kv 16: qwen2.embedding_length u32 = 3584
llama_model_loader: – kv 17: qwen2.feed_forward_length u32 = 18944
llama_model_loader: – kv 18: qwen2.attention.head_count u32 = 28
llama_model_loader: – kv 19: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: – kv 20: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 22: general.file_type u32 = 7
llama_model_loader: – kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 28: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 30: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 33: general.quantization_version u32 = 2
llama_model_loader: – kv 34: quantize.imatrix.file str = /models_out/Qwen2.5-7B-Instruct-GGUF/…
llama_model_loader: – kv 35: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: – kv 36: quantize.imatrix.entries_count i32 = 196
llama_model_loader: – kv 37: quantize.imatrix.chunks_count i32 = 128
llama_model_loader: – type f32: 141 tensors
llama_model_loader: – type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 7.54 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 3584
print_info: n_layer = 28
print_info: n_head = 28
print_info: n_head_kv = 4
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 7
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 18944
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: model type = 7B
print_info: model params = 7.62 B
print_info: general.name = Qwen2.5 7B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 152064
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 28 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CUDA0 model buffer size = 7165.44 MiB
load_tensors: CPU_Mapped model buffer size = 552.23 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 16384
llama_context: n_ctx_per_seq = 16384
llama_context: n_batch = 64
llama_context: n_ubatch = 64
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_per_seq (16384) < n_ctx_train (32768) — the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 896.00 MiB
llama_kv_cache: size = 896.00 MiB ( 16384 cells, 28 layers, 1/1 seqs), K (f16): 448.00 MiB, V (f16): 448.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 43.75 MiB
llama_context: CUDA_Host compute buffer size = 4.88 MiB
llama_context: graph nodes = 959
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|im_end|> logit bias = -inf
common_init_from_params: added <|fim_pad|> logit bias = -inf
common_init_from_params: added <|repo_name|> logit bias = -inf
common_init_from_params: added <|file_sep|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 16384
common_init_from_params: warming up the model with an empty run – please wait … (–no-warmup to disable)
main: llama threadpool init, n_threads = 8

sampler seed: 402798147
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 16384
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 16384, n_batch = 64, n_predict = 128, n_keep = 0

test = { 'name': 'q1_1',
'points': 1,
'suites': [ { 'cases': [ {'code': '>>> isinstance(largest, float) and isinstance(largest, float)\nTrue', 'hidden': False, 'locked': False},
{'code': '>>> 6.0 <= largest <= 7.0\nTrue', 'hidden': False, 'locked': False},
{'code': '>>> 4.0 <= largest <= 5.0\nTrue', 'hidden': False, 'locked': False}],
'scored

llama_perf_sampler_print: sampling time = 14.65 ms / 129 runs ( 0.11 ms per token, 8805.46 tokens per second)
llama_perf_context_print: load time = 1730.42 ms
llama_perf_context_print: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: eval time = 3242.90 ms / 128 runs ( 25.34 ms per token, 39.47 tokens per second)
llama_perf_context_print: total time = 3296.15 ms / 129 tokens
llama_perf_context_print: graphs reused = 127
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T14:55:35-07:00 · gpt-5-thinking

Beautiful. That’s a successful Q8 @ 16k run ✅

What your log shows

It fits: model buffer ~7.0–7.2 GiB + KV cache 896 MiB @ 16k → ~8.5–9 GiB total VRAM while idle/infer. Plenty of headroom on 12 GB.
Speed: ~39.5 tok/s at -c 16384 -b 64 (about the same as your Q8 @ 4k run because the prompt was tiny; throughput mainly depends on quant + batch).
Stability: No OOM, Flash-Attn enabled, all 29 layers offloaded. 🎯

Suggested presets (copy-paste)

Daily (fast, 4k, Q5):

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf \
  -ngl 999 -c 4096 -b 256 -t $(nproc)

Quality (4k, Q8):

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 4096 -b 128 -t $(nproc)

Stretch Memory (16k, Q8, safe):

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 16384 -b 64 -t $(nproc)

If 16k ever feels tight, drop batch (-b 32) or push KV to RAM (slower):

... -c 16384 -b 32 --no-kv-offload

Reduce repetition (optional)

--repeat-penalty 1.1 --repeat-last-n 256
# or:
--mirostat 2 --mirostat-lr 0.1 --mirostat-ent 5

Quick benchmark one-shot (prints tokens/sec)

# 4k Q8
./build/bin/llama-cli -m models/...Q8_0.gguf -ngl 999 -c 4096 -b 128 -no-cnv -n 192 -p &quot;test&quot;

# 16k Q8
./build/bin/llama-cli -m models/...Q8_0.gguf -ngl 999 -c 16384 -b 64 -no-cnv -n 128 -p &quot;test&quot;

TL;DR

Q5 (4k) = best daily speed;
Q8 (4k) = nicer polish at ~25–30% slower;
Q8 (16k) works on your 3060 with -b 64, ~39 tok/s for short prompts—great for occasional long-turn chats/docs.
For truly long docs, we’ll still rely on RAG so you don’t pay 16k costs every time.

Want me to drop shell aliases (oogway-q5, oogway-q8, oogway-q8-16k) into a snippet you can paste into ~/.bashrc?

Darren2025-09-18T14:57:46-07:00

Okay, let's run it with a fully open pipe, 32k, and I want to drop like a couple of pages in and let's see what happens.

ChatGPT2025-09-18T14:57:48-07:00 · gpt-5

Got it — you want to push the Q8 model at max context (32k) and actually shove a couple pages of text in, not just a single word prompt.

Your 3060 has 12 GB VRAM, and the math looks like this:

KV cache at 32k ≈ ~1.75 GB (twice what you saw at 16k).
Model buffer ≈ ~7.2 GB.
Overheads (CUDA buffers, host cache, graphs, etc.) ≈ 2–3 GB.
Total ≈ 11–12 GB. This is right at the edge, but doable with -b 32 (and no large batch).

Try this run:

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 512 \
  -p &quot;$(cat your_text.txt)&quot;

🔹 Replace your_text.txt with a file containing the “couple of pages” you want to feed in.
🔹 -b 32 keeps KV memory down.
🔹 -n 512 just caps max generation (adjust if you want more).

⚠️ Expectations:
– It will be slower (maybe ~20 tok/s or less).
– First run will warm up and allocate the ~12 GB, so don’t panic if VRAM spikes.
– If it OOMs:
1. Drop to -b 16.
2. Add --no-kv-offload (falls back to RAM, slower but safe).

Want me to prep you a ready-made test file (couple pages of sample text) so you can just cat it in and verify the 32k pipeline works, before you drop in your own doc?

Darren2025-09-18T15:00:50-07:00

Well, you know, I don't actually have a file on this machine just yet. How about in the interest of speed and time, um, give me a new instruction set that I can just drop some text that I copy and paste? Yeah.

ChatGPT2025-09-18T15:00:55-07:00 · gpt-5-thinking

Perfect—let’s do a 32k run where you can just paste text right in the terminal (no files needed).

Option A — paste with a heredoc (my favorite)

1) Copy this whole block, hit Enter, then paste your pages between PASTE BELOW and EOF, press Enter, then type EOF on its own line and press Enter again:

cd ~/ougway_env/llama.cpp

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 384 \
  -p &quot;$(cat &lt;&lt;&#x27;EOF&#x27;
SYSTEM:
You are Oogway. Read the CONTEXT and then produce a concise summary (&lt;=150 words) and 5 key takeaways. If something is unclear, list 3 clarifying questions.

CONTEXT:
PASTE BELOW
EOF
)&quot;

After you paste your text, finish with a line that contains only EOF.
This will run once and exit, printing the perf stats at the end.

Option B — same idea, but store the paste in a bash variable first

cd ~/ougway_env/llama.cpp

read -r -d &#x27;&#x27; OOGWAY_INPUT &lt;&lt;&#x27;EOF&#x27;
PASTE YOUR PAGES HERE
EOF

./build/bin/llama-cli \
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 384 \
  -p &quot;SYSTEM:
You are Oogway. Read the CONTEXT and then produce a concise summary (&lt;=150 words) and 5 key takeaways. If unclear, list 3 clarifying questions.

CONTEXT:
$OOGWAY_INPUT&quot;

If VRAM gets tight or you see OOM

Try these in order:
– Drop batch: -b 32 → -b 16 → -b 8
– Keep KV cache in RAM (slower but safe): add --no-kv-offload
– As last resort, offload fewer layers: -ngl 64

Example “safe mode”:

./build/bin/llama-cli -m models/...Q8_0.gguf \
  -ngl 64 -c 32768 -b 16 --no-kv-offload -t $(nproc) -no-cnv -n 256 \
  -p &quot;...\n$OOGWAY_INPUT&quot;

Tips

Watch VRAM in another tab: watch -n 1 nvidia-smi
For longer pastes, you can bump -n up or down. Lower -n keeps you under the 32k cap more easily.
If the model gets repetitive on big context, add:
--repeat-penalty 1.1 --repeat-last-n 256
Want Q&A instead of a summary? Change the prompt tail to:
After reading CONTEXT, answer the user’s question: "<your question here>". If the answer isn’t in CONTEXT, say so.

Paste your pages and go—if anything errors, tell me the message and your peak VRAM and I’ll tweak the flags.

Darren2025-09-18T15:08:26-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ cd ~/ougway_env/llama.cpp

./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 384 \
-p "$(cat <<'EOF'
SYSTEM:
You are Oogway. Read the CONTEXT and then produce a concise summary (<=150 words) and 5 key takeaways. If something is unclear, list 3 clarifying questions.

CONTEXT:
art 1: Laying the 2D Foundation – The Seed of Life
1. Start with the Center: Begin by drawing a single, perfect circle on a flat surface. This is
your core, your initial point of creation.
2. Expand with Six Neighbors: Using the exact radius of your first circle, place your compass
point on any spot on its circumference. From that point, draw a second circle that passes
precisely through the center of your first circle. You will notice it naturally intersects the first
circle at two points.
3. Continue the Pattern: Now, shift your compass point to one of these new intersection
points. Again, draw a circle of the same radius. Repeat this process, moving your compass grow outward from the previous one, maintaining the same geometric proportionsill naturall
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
build: 6511 (4ca088b0) with cc (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 3060) (0000:01:00.0) – 11601 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 339 tensors from models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen2
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen2.5 7B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen2.5
llama_model_loader: – kv 5: general.size_label str = 7B
llama_model_loader: – kv 6: general.license str = apache-2.0
llama_model_loader: – kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7…
llama_model_loader: – kv 8: general.base_model.count u32 = 1
llama_model_loader: – kv 9: general.base_model.0.name str = Qwen2.5 7B
llama_model_loader: – kv 10: general.base_model.0.organization str = Qwen
llama_model_loader: – kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-7B
llama_model_loader: – kv 12: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: – kv 13: general.languages arr[str,1] = ["en"]
llama_model_loader: – kv 14: qwen2.block_count u32 = 28
llama_model_loader: – kv 15: qwen2.context_length u32 = 32768
llama_model_loader: – kv 16: qwen2.embedding_length u32 = 3584
llama_model_loader: – kv 17: qwen2.feed_forward_length u32 = 18944
llama_model_loader: – kv 18: qwen2.attention.head_count u32 = 28
llama_model_loader: – kv 19: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: – kv 20: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 22: general.file_type u32 = 7
llama_model_loader: – kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 28: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 30: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 33: general.quantization_version u32 = 2
llama_model_loader: – kv 34: quantize.imatrix.file str = /models_out/Qwen2.5-7B-Instruct-GGUF/…
llama_model_loader: – kv 35: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: – kv 36: quantize.imatrix.entries_count i32 = 196
llama_model_loader: – kv 37: quantize.imatrix.chunks_count i32 = 128
llama_model_loader: – type f32: 141 tensors
llama_model_loader: – type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 7.54 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 3584
print_info: n_layer = 28
print_info: n_head = 28
print_info: n_head_kv = 4
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 7
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 18944
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: model type = 7B
print_info: model params = 7.62 B
print_info: general.name = Qwen2.5 7B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 152064
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 28 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CUDA0 model buffer size = 7165.44 MiB
load_tensors: CPU_Mapped model buffer size = 552.23 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_batch is less than GGML_KQ_MASK_PAD – increasing to 64
llama_context: n_seq_max = 1
llama_context: n_ctx = 32768
llama_context: n_ctx_per_seq = 32768
llama_context: n_batch = 64
llama_context: n_ubatch = 64
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 1000000.0
llama_context: freq_scale = 1
llama_context: CUDA_Host output buffer size = 0.58 MiB
llama_kv_cache: CUDA0 KV buffer size = 1792.00 MiB
llama_kv_cache: size = 1792.00 MiB ( 32768 cells, 28 layers, 1/1 seqs), K (f16): 896.00 MiB, V (f16): 896.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 47.75 MiB
llama_context: CUDA_Host compute buffer size = 8.88 MiB
llama_context: graph nodes = 959
llama_context: graph splits = 2
common_init_from_params: added <|endoftext|> logit bias = -inf
common_init_from_params: added <|im_end|> logit bias = -inf
common_init_from_params: added <|fim_pad|> logit bias = -inf
common_init_from_params: added <|repo_name|> logit bias = -inf
common_init_from_params: added <|file_sep|> logit bias = -inf
common_init_from_params: setting dry_penalty_last_n to ctx_size = 32768
common_init_from_params: warming up the model with an empty run – please wait … (–no-warmup to disable)
main: llama threadpool init, n_threads = 8

sampler seed: 1821317760
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 32768
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 32768, n_batch = 32, n_predict = 384, n_keep = 0

SYSTEM:
You are Oogway. Read the CONTEXT and then produce a concise summary (<=150 words) and 5 key takeaways. If something is unclear, list 3 clarifying questions.

CONTEXT:
art 1: Laying the 2D Foundation – The Seed of Life
1. Start with the Center: Begin by drawing a single, perfect circle on a flat surface. This is
your core, your initial point of creation.
2. Expand with Six Neighbors: Using the exact radius of your first circle, place your compass
point on any spot on its circumference. From that point, draw a second circle that passes
precisely through the center of your first circle. You will notice it naturally intersects the first
circle at two points.
3. Continue the Pattern: Now, shift your compass point to one of these new intersection
points. Again, draw a circle of the same radius. Repeat this process, moving your compass to
each new intersection point you create, drawing a new circle with the same radius, always
ensuring it passes through the center of the previous circle.
4. Complete the Flower: After drawing six circles around your original central circle, you will
see a pattern resembling a flower with six petals. This is the “Seed of Life.” If you continue
outward, using the intersections of these circles to draw more, you will expand the classic
2D Flower of Life. Continue this expansion for at least 3-4 layers of petals.
Part 2: Elevating to 3D – The Sphere of Life
1. Visualize as Spheres: Now, conceptualize each of those perfect 2D circles as a perfect 3D
sphere. Imagine them as translucent, interlocking bubbles.
2. Interlocking Formations: Where your 2D circles overlapped, your 3D spheres now
interpenetrate, forming stable, interconnected clusters. The central cluster will be a dense
core of spheres.
3. Stacking Principle: Imagine these spheres not just on a flat plane, but extending upward
and downward. Each sphere sits in the “dimple” created by three spheres below it, forming a
hexagonal close-packed (HCP) or face-centered cubic (FCC) arrangement. This natural
stacking method gives your flat 2D pattern depth.
Part 3: The First Encapsulation – Containing the Initial Bloom
1. Identify the Outer Limits: Once you have a core cluster of these 3D spheres (e.g., the
central sphere, its 12 immediate neighbors in 3D, and the spheres that naturally form around
them), identify the absolute outermost points of this entire cluster.
2. Draw the Bounding Sphere: Imagine or compute a single, larger transparent sphere that
perfectly encloses every single one of the spheres in your initial 3D Flower of Life cluster.
This is your first “encapsulation sphere”—it defines the initial boundary of your growing
lattice.
Part 4: Iterative Expansion – Building Out 32 Encapsulation Layers
1. Concentric Growth: From this first encapsulation sphere, your lattice expands
concentrically, layer by layer. Each new layer of the Flower of Life lattice will naturally
grow outward from the previous one, maintaining the same geometric proportions.
2. Layer by Layer: Think of each layer as a step outward, increasing the radius of the
encapsulation sphere by a consistent fraction of the original sphere's radius with each
iteration.
3. Iterative Process: Repeat the process of adding new spheres and drawing new
encapsulation spheres for a total of 32 layers, each layer expanding the lattice and
increasing the complexity of the pattern.
Summary and Key Takeaways:
Summary:
The Seed of Life is a 2D geometric pattern made by drawing 7 perfect circles of the same radius, with each circle centered on the circumference of another. This pattern is then elevated to 3D by imagining each circle as a sphere, which interlock to form a cluster. The initial cluster is then enclosed in a bounding sphere, and the pattern is iteratively expanded, layer by layer, to create a complex, interconnected lattice of 32 encapsulation layers.

Key Takeaways:
1. The Seed of Life is a foundational pattern for understanding more complex geometric patterns.
2. Interlocking spheres in 3D provide a stable, hexagonal close-packed arrangement.
3. The process of encapsulation defines the initial boundary of the geometric pattern.
4. Iterative expansion maintains consistent proportions and complexity.
5. Each layer of the pattern adds more spheres, increasing the overall complexity of the lattice.

Clarifying Questions:
1. How does the radius of the spheres in 3D relate to the radius of the initial 2D circles?
2. What is the significance of the hexagonal close-packed arrangement in the 3D spheres?
3. How is the radius of the encapsulation sphere determined for each layer?
Summary:
The Seed of Life is a 2D pattern made of 7 perfect circles, forming a flower shape, which is then transformed into 3D by imagining each circle as a sphere. This 3D formation

llama_perf_sampler_print: sampling time = 44.12 ms / 1071 runs ( 0.04 ms per token, 24276.36 tokens per second)
llama_perf_context_print: load time = 1718.38 ms
llama_perf_context_print: prompt eval time = 701.60 ms / 687 tokens ( 1.02 ms per token, 979.19 tokens per second)
llama_perf_context_print: eval time = 9810.59 ms / 383 runs ( 25.62 ms per token, 39.04 tokens per second)
llama_perf_context_print: total time = 10646.86 ms / 1070 tokens
llama_perf_context_print: graphs reused = 398
(venv) darren@ougway:~/ougway_env/llama.cpp$

It failed with about six pages because it went over by about two pages. But with a single page, this is what we got. Check it out.

ChatGPT2025-09-18T15:08:34-07:00 · gpt-5-thinking

Heck yes — your 32k Q8 run worked, and the numbers look great:

Prompt ingest: ~979 tok/s
Gen speed: ~39 tok/s
KV @ 32k: 1792 MiB (fits your 12 GB card with headroom)

About the 6-page paste “failing”: that’s just the context budget. The model’s window must fit:

> system/prompt tokens + your pasted text tokens + generated tokens ≤ 32768

When it’s over, llama.cpp stops early (what you saw). Two easy ways to avoid this:

Option A — Quick “preflight” to size your paste

1) Run once with -n 1 to measure prompt tokens:

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
  -ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 1 \
  -p &quot;$(cat &lt;&lt;&#x27;EOF&#x27;
SYSTEM:
You are Oogway. Summarize and give 5 takeaways.

CONTEXT:
PASTE YOUR BIG TEXT HERE
EOF
)&quot;

At the end, look for:

prompt eval time = ... /   &lt;THIS_NUMBER&gt; tokens

2) Budget your output:

max_output ≈ 32760 - &lt;prompt_tokens&gt; - 128   # keep a little margin

3) Re-run with -n <max_output>.

Option B — Rough sizing by characters (no dry run)

Rule of thumb: ~4 chars ≈ 1 token (English).
If your paste is ~100k chars, that’s ~25k tokens — with a 500–1000-token prompt and some output, you’re near the 32k ceiling. Trim or split.

Handy split for long texts

Break the paste into manageable chunks and run sequentially:

# split roughly by bytes (≈ characters). tweak 90k up/down as needed
split -b 90000 --additional-suffix=.part big.txt chunk_

# then for each chunk_*
./build/bin/llama-cli -m ...Q8_0.gguf -ngl 999 -c 32768 -b 32 -t $(nproc) -no-cnv -n 384 \
  -p &quot;SYSTEM: You are Oogway. Summarize this chunk in ≤150 words, then list 5 key takeaways.\n\nCONTEXT:\n$(cat chunk_aa)&quot;

Small quality-of-life tweaks

Avoid repeats on long inputs:
--repeat-penalty 1.1 --repeat-last-n 256
If VRAM gets tight at 32k:
Drop batch: -b 32 → 16 → 8
Last resort (slower, but safe): --no-kv-offload (stores KV in RAM)

If you want, paste the prompt token count from your -n 1 preflight and I’ll compute a safe -n for your full 6-page input.