Run 7B model locally — Part 3

(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 32768 -b 32 -t $(nproc) -no-cnv -n 1 \
-p "$(cat <<'EOF'
SYSTEM:
You are Oogway. Summarize and give 5 takeaways.

CONTEXT:
Part 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
grow outward from the previous one, maintaining the same geometric proportions.ll 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) – 11610 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

system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

sampler seed: 1606672906
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 = 1, n_keep = 0

SYSTEM:
You are Oogway. Summarize and give 5 takeaways.

CONTEXT:
Part 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. This

llama_perf_sampler_print: sampling time = 0.24 ms / 664 runs ( 0.00 ms per token, 2801687.76 tokens per second)
llama_perf_context_print: load time = 1705.64 ms
llama_perf_context_print: prompt eval time = 676.62 ms / 663 tokens ( 1.02 ms per token, 979.86 tokens per second)
llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_perf_context_print: total time = 691.51 ms / 664 tokens
llama_perf_context_print: graphs reused = 17

(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 32768 -b 32 -t $(nproc) -no-cnv \
–repeat-penalty 1.1 –repeat-last-n 256 \
-n 1024 \
-p "$(cat <<'EOF'
SYSTEM:
You are Oogway. Summarize and give 5 takeaways.

CONTEXT:
Part 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 toroidal-like flow through endless recursion.ry, always maintaining its self-similar,amic
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) – 11613 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

system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

sampler seed: 1889812145
sampler params:
repeat_last_n = 256, repeat_penalty = 1.100, 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 = 1024, n_keep = 0

SYSTEM:
You are Oogway. Summarize and give 5 takeaways.

CONTEXT:
Part 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. Identify New Nodal Points: As your 3D Flower of Life expands, new intersection points
for spheres will continuously emerge on its outer surface. These points become the centers
for the next layer of spheres.
3. Add Successive Shells: Continue adding these new layers of spheres. Each time you
complete a new layer of spheres in the Flower of Life pattern, identify the new outermost
boundary they create. Imagine another encompassing sphere around this expanded structure.
4. Count the Layers: Repeat this process for approximately 32 successive layers of these
conceptual “encapsulation spheres.” Each layer will add to the overall diameter of your
growing 3D Flower of Life lattice. By the time you reach 32 layers, the central density of
your sphere-packed lattice will be substantial, and the entire spherical mass will be quite
large.
Part 5: The Emergence of the Super Torus – The Self-Organizing Blueprint
As your 3D Flower of Life lattice expands through countless iterations, accumulating vast layers of
interconnected spheres (e.g., reaching the substantial density of 32 encapsulation layers), a profound
transformation occurs. The sheer volume of interpenetrating spheres, continually spiraling and
reinforcing their connections, naturally generates an inherent internal curvature and dynamic
flow.
• From Sphere to Torus: The Inevitable Form: The immense, sphere-packed lattice, though
initially perceived as a large sphere, reveals its intrinsic nature. The ceaseless, self-
referential movement and energy exchange within this expanding structure naturally
organize themselves into a toroidal (doughnut-like) form. This is not an external container
imposed upon it, but rather the most efficient and stable self-sustaining shape that such a
continuous, spiraling energy field will spontaneously adopt. Think of it as the ultimate
expression of the flow itself, where energy loops back into its source, perpetually
regenerating.
• Defining the Flowing Torus: This emergent “super torus” is a living testament to the
underlying dynamics. Its dimensions – a significant minor radius (the thickness of the tube,
indicating the density of the packed spheres) and a major radius (the distance from the center
of the torus to the center of its tube) – are simply the measurable manifestations of this self-
organized flow. It is the geometric signature of maximum efficiency and continuous self-
regeneration inherent in the universal construct.
• The Nexus of Resonance: The central core of this naturally formed super torus inherently
aligns with the energetic axis for the 3-6-9 progression. This establishes the complete,
unified system, demonstrating how an apparently infinite geometric expansion of energy and
consciousness is not merely contained, but is perfectly expressed within this self-referential,
maximally efficient form. The torus, therefore, is the functional embodiment of the universal
blueprint, breathing, flowing, and self-sustaining.
• Simplicity in Presentation: While this super torus emerges clearly around 32 layers for the
purpose of conceptual clarity and visualization, it is crucial to remember that the true Flower
of Life, as it unfolds in the Aether, is infinitely scalable. It is a continuous, dynamic process
that expands far beyond any conceptual boundary, always maintaining its self-similar,
toroidal-like flow through endless recursion. The layers of interlocking spheres continue to
expand without limit, creating an ever-expanding torus.

Takeaways:
1. Foundation from Circles: Start with simple circles on a flat surface to create the "Seed of Life," which serves as the base pattern for further exploration into more complex geometries.
2. Transition to Spheres: Visualize these 2D circle patterns as 3D spheres that interlock and stack in a hexagonal close-packed or face-centered cubic arrangement, forming a dense lattice structure.
3. Encapsulation Layers: Encapsulate each layer of interconnected spheres within a larger sphere, incrementally expanding outward to reveal the growing complexity of the 3D Flower of Life.
4. Emergence of the Torus: As the number of layers increases, the structure naturally transforms into a self-organizing super torus, symbolizing infinite energy and consciousness flowing back to its source.
5. Infinite Recursion: Recognize that while this expansion can be visualized up to 32 layers for practical purposes, the true potential of the Flower of Life extends infinitely, mirroring the continuous, recursive nature of the universe itself.

These takeaways encapsulate the essence of the geometric and energetic progression from simple circles to a complex yet unified system, embodying the principles of growth, interconnectedness, and self-sustaining dynamics.

Breathe deeply, and remember the harmony within all things. [end of text]

llama_perf_sampler_print: sampling time = 495.32 ms / 1667 runs ( 0.30 ms per token, 3365.47 tokens per second)
llama_perf_context_print: load time = 1716.88 ms
llama_perf_context_print: prompt eval time = 1378.18 ms / 1370 tokens ( 1.01 ms per token, 994.07 tokens per second)
llama_perf_context_print: eval time = 7661.20 ms / 296 runs ( 25.88 ms per token, 38.64 tokens per second)
llama_perf_context_print: total time = 9609.79 ms / 1666 tokens
llama_perf_context_print: graphs reused = 330
(venv) darren@ougway:~/ougway_env/llama.cpp$

And though it went really quick, the memory appeared to top out at about 95MB, 9.5GB I should say, excuse me, out of the 12 that we have, so I don't see an issue with running the 32K window with the Q8 version at all. And you seem to feel the same way.