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RAG and LoRa setup — Part 3

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A conversation worth keeping.

The original exchange, kept in context—ready to read, revisit, and explore.

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Darren2025-09-19T22:12:51-07:00

darren@ougway:~/Desktop$ /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
-m /home/darren/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf \
-ngl 999 -c 32768 -b 16 -t $(nproc) -n 512 \
-no-cnv -f stress_context.txt
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) – 11559 MiB free
llama_model_loader: loaded meta data with 34 key-value pairs and 399 tensors from /home/darren/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.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 = qwen3
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen3 8B
llama_model_loader: – kv 3: general.basename str = Qwen3
llama_model_loader: – kv 4: general.size_label str = 8B
llama_model_loader: – kv 5: general.license str = apache-2.0
llama_model_loader: – kv 6: general.license.link str = https://huggingface.co/Qwen/Qwen3-8B/…
llama_model_loader: – kv 7: general.base_model.count u32 = 1
llama_model_loader: – kv 8: general.base_model.0.name str = Qwen3 8B Base
llama_model_loader: – kv 9: general.base_model.0.organization str = Qwen
llama_model_loader: – kv 10: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3-8B-…
llama_model_loader: – kv 11: general.tags arr[str,1] = ["text-generation"]
llama_model_loader: – kv 12: qwen3.block_count u32 = 36
llama_model_loader: – kv 13: qwen3.context_length u32 = 40960
llama_model_loader: – kv 14: qwen3.embedding_length u32 = 4096
llama_model_loader: – kv 15: qwen3.feed_forward_length u32 = 12288
llama_model_loader: – kv 16: qwen3.attention.head_count u32 = 32
llama_model_loader: – kv 17: qwen3.attention.head_count_kv u32 = 8
llama_model_loader: – kv 18: qwen3.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 19: qwen3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 20: qwen3.attention.key_length u32 = 128
llama_model_loader: – kv 21: qwen3.attention.value_length u32 = 128
llama_model_loader: – kv 22: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 23: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 24: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 25: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 26: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 27: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 28: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 29: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 30: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 31: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 32: general.quantization_version u32 = 2
llama_model_loader: – kv 33: general.file_type u32 = 18
llama_model_loader: – type f32: 145 tensors
llama_model_loader: – type q6_K: 254 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q6_K
print_info: file size = 6.26 GiB (6.56 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 = 26
load: token to piece cache size = 0.9311 MB
print_info: arch = qwen3
print_info: vocab_only = 0
print_info: n_ctx_train = 40960
print_info: n_embd = 4096
print_info: n_layer = 36
print_info: n_head = 32
print_info: n_head_kv = 8
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 = 4
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
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 = 12288
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 = 40960
print_info: rope_finetuned = unknown
print_info: model type = 8B
print_info: model params = 8.19 B
print_info: general.name = Qwen3 8B
print_info: vocab type = BPE
print_info: n_vocab = 151936
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 36 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 37/37 layers to GPU
load_tensors: CUDA0 model buffer size = 5921.78 MiB
load_tensors: CPU_Mapped model buffer size = 486.86 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: n_ctx_per_seq (32768) < n_ctx_train (40960) — 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 = 4608.00 MiB
llama_kv_cache: size = 4608.00 MiB ( 32768 cells, 36 layers, 1/1 seqs), K (f16): 2304.00 MiB, V (f16): 2304.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 38.09 MiB
llama_context: CUDA_Host compute buffer size = 9.00 MiB
llama_context: graph nodes = 1267
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 |

main: prompt is too long (750034 tokens, max 32764)
darren@ougway:~/Desktop$

ChatGPT2025-09-19T22:12:53-07:00 · gpt-5-thinking

Ah—your prompt blew past the 32k cap. That 30,000-line filler produced ~750,034 tokens 😅 (each long line is dozens of tokens, so it explodes fast). You just need much fewer lines.

Here’s a clean redo that stays under 32k and still stress-tests:

1) Rebuild a right-sized context (~24–28k tokens)

Use ~1,100–1,200 lines of that sentence (≈ ~20–25 tokens/line × 1.2k ≈ 24–30k tokens total).

# Make a ~24–28k token filler (adjust N if needed)
N=1200
yes &#x27;The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This is filler text for stress testing long contexts.&#x27; \
  | head -n $N &gt; filler_approx24k.txt

printf &#x27;--- START CONTEXT ---\n&#x27; &gt; stress_context.txt
cat filler_approx24k.txt &gt;&gt; stress_context.txt
cat &gt;&gt; stress_context.txt &lt;&lt;&#x27;EOF&#x27;
--- END CONTEXT ---
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above.
Then list 3 questions that remain unanswered.
EOF

2) Run without chat templating (no formatter issues)

/home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf \
  -ngl 999 -c 32768 -b 16 -t $(nproc) -n 512 \
  -no-cnv --verbose-prompt -f stress_context.txt
  • --verbose-prompt will print the prompt token count up front so you can confirm it’s < 32764.

If token count is still high

  • Drop N to 1000 (≈ ~20–25k tokens), or
  • Shorten the filler sentence (fewer words → fewer tokens), e.g.:
    bash
    N=15000
    yes &#x27;Symmetry and boundary conditions matter for the Flower Lattice.&#x27; | head -n $N &gt; filler_short.txt
    (Shorter lines let you use more lines while staying under 32k.)

Bonus: super-deterministic token sizing

If you want to guarantee a certain token count, use tiny “words”:

awk &#x27;BEGIN{for(i=1;i&lt;=32000;i++) printf &quot;a &quot;}&#x27; &gt; filler_32k_words.txt

That’s roughly one token per “a” (BPE-ish), so it lands very close to 32k. Then wrap it with the same START/END CONTEXT and question and run with -no-cnv --verbose-prompt. If it’s a tad over, reduce to 31000.

Once this runs, watch nvidia-smi—you should see ~~5.9 GiB (model) + ~4.6 GiB (KV for 32k) + ~tens of MiB overhead, keeping you under the 12 GB ceiling with some headroom.

Darren2025-09-19T22:16:25-07:00

11090mib — END CONTEXT —
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above.
Then list 3 questions that remain unanswered.

— START ANSWER —
The context primarily discusses symmetry, frequency domains, and boundary conditions in the context of the Flower Lattice. Here are five key ideas:

  1. Symmetry plays a crucial role in analyzing the Flower Lattice, influencing its structure and properties.
  2. Frequency domains are mentioned, likely related to the lattice's vibrational or wave behavior.
  3. Boundary conditions are a central topic, affecting the lattice's behavior and solutions.
  4. The text emphasizes repetition and patterns in the lattice, which are tied to symmetry and frequency analysis.
  5. Stress testing with long contexts suggests the need for detailed exploration of these concepts in practical applications.

Unanswered questions:
– How exactly do symmetry and boundary conditions interact in the Flower Lattice?
– What specific frequency domains are relevant to the Flower Lattice's analysis?
– How do these concepts apply to real-world scenarios or engineering applications?
— END ANSWER —

The Flower Lattice primer emphasizes the importance of symmetry in shaping the lattice's structure and properties, with symmetry influencing patterns and repetition. Frequency domains are linked to vibrational or wave behaviors, suggesting analysis in different frequency ranges. Boundary conditions are highlighted as critical for determining the lattice's behavior and solutions, impacting how the system responds to external factors. The text also implies that these concepts are interrelated, with symmetry affecting how boundary conditions are applied and how frequencies propagate. Finally, the mention of stress testing with long contexts underscores the need for thorough exploration of these ideas in practical applications.

Unanswered questions:
1. How do symmetry and boundary conditions interact specifically within the Flower Lattice?
2. What exact frequency domains are relevant to the Flower Lattice's analysis?
3. How can these concepts be applied to real-world engineering or scientific problems?

This summary and questions reflect the key points and lingering uncertainties from the context provided.

— END ANSWER —
The context discusses symmetry, frequency domains, and boundary conditions in the context of the Flower Lattice. Here are five key ideas:

  1. Symmetry is central to the structure and properties of the Flower Lattice, influencing patterns and repetition.
  2. Frequency domains are relevant to analyzing vibrational or wave behavior in the lattice.
  3. Boundary conditions are critical for determining the lattice's behavior and solutions.
  4. Symmetry and frequency domains are tied to the lattice's structural and dynamic properties.
  5. The text

llama_perf_sampler_print: sampling time = 65.82 ms / 30546 runs ( 0.00 ms per token, 464076.81 tokens per second)
llama_perf_context_print: load time = 1569.53 ms
llama_perf_context_print: prompt eval time = 76761.73 ms / 30034 tokens ( 2.56 ms per token, 391.26 tokens per second)
llama_perf_context_print: eval time = 18624.08 ms / 511 runs ( 36.45 ms per token, 27.44 tokens per second)
llama_perf_context_print: total time = 95680.32 ms / 30545 tokens
llama_perf_context_print: graphs reused = 2267
darren@ougway:~/Desktop$

ChatGPT2025-09-19T22:16:28-07:00 · gpt-5-thinking

Boom — that’s a clean pass. ✅

What your run shows

  • VRAM: ~11.09 GiB used (“11090 MiB”) → exactly what we expected for Q6_K @ 32k (≈5.9 GiB model + ≈4.6 GiB KV + buffers). You’ve got ~1 GiB headroom.
  • Prompt length: ~30k tokens (see prompt eval time = … / 30034 tokens) — so you actually exercised the big window.
  • Speed: ~391 tok/s for prompt ingest, ~27.4 tok/s for generation — solid for a 3060 12 GB at this context.
  • Stability: no OOM, no throttle — mechanics are solid.

Why the answer repeats / looks generic

You fed near-identical lines and ran -no-cnv (no chat template), so it did a literal summarization of repetitive text. That’s fine for a stress test, but for “real” behavior you’ll want either varied content or the chat template.

If you want a “realistic” long-context test

Keep the big body in a file, but turn chat templating back on and keep the instruction short:

# stress_context.txt should contain ONLY the big context section, no question.
# e.g., just the repeated lines or your real doc.

# Then run with a small chat prompt:
 /home/darren/ougway_env/llama.cpp/build/bin/llama-cli \
  -m /home/darren/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf \
  -ngl 999 -c 32768 -b 16 -t $(nproc) -n 400 --jinja \
  -sys &quot;You are Ougway — warm, concise, helpful.&quot; \
  -p &quot;You will receive a long context next. Read it, then answer the question.&quot; \
  -f stress_context.txt \
  -p &quot;Question: In 5 bullets, summarize key ideas about symmetry and boundary conditions. Then list 3 open questions.&quot;

That keeps the persona while avoiding the template crash you hit earlier (since the big chunk is in -f, not -p).

Optional tuning knobs (only if you need them)

  • Want a bit more throughput? Try -b 32 (batch) if VRAM allows; watch nvidia-smi.
  • If you ever push 40k with Q6_K: expect borderline OOM. You can try -c 40960 -b 8 -ngl 30 (moves a few layers to CPU) — slower, but may squeeze by.

If you want, I can package this as two aliases: ougway6-32k (Q6_K long-context) and ougway8-24k (Q8_0 quality mode), so switching is one command.

Darren2025-09-19T22:19:11-07:00

Now, I think what I need now is a simple output of the results of our tests, one for the Q6 version and one for the Q8 version, and show where the Q8 version needs to be dealt with a little. In other words, 16k context window as opposed to 32, whereas the Q6 can be run wide open, and something I can post on Farsight.as well as any key cafe.

ChatGPT2025-09-19T22:19:13-07:00 · gpt-5

Got it 👍 — here’s a clear, post-ready summary of your results for Qwen3-8B Q6_K vs Q8_0 on your RTX 3060 12 GB. I’ll make it clean, simple, and forum-ready.

🧪 Stress Test Results — Qwen3-8B on RTX 3060 12 GB

We tested both Q6_K and Q8_0 quantizations of Qwen3-8B using llama.cpp, pushing context size and measuring stability.

✅ Q6_K (6.26 GiB model file)

  • Context window: Stable at 32k tokens (full usable range on 12 GB VRAM).
  • VRAM usage: ~11.1 GiB (fits cleanly with ~1 GB headroom).
  • Prompt ingest speed: ~391 tokens/sec (at 30k tokens).
  • Generation speed: ~27 tokens/sec.
  • Notes: Best choice for max context length. Runs wide open on a 3060.

⚖️ Q8_0 (8.11 GiB model file)

  • Context window: Practical max around 16–24k tokens.
  • At 24k: Stable, but VRAM nearly maxed (~11.7 GiB of 12 GB).
  • At 32k: Too tight — risk of OOM.
  • Performance: Slightly better quality (higher precision), but smaller safe window.
  • Notes: Best choice if you want quality over max length. Needs careful context trimming.

🔑 Takeaways

  • Q6_K: Use for full long-context runs (32k). Stable and efficient.
  • Q8_0: Use for shorter, higher-fidelity runs (≤24k). Needs stricter memory budgeting.
  • Recommendation: Keep both builds available and switch depending on whether you need maximum memory (Q6_K) or maximum accuracy (Q8_0).

That’s the distilled comparison you can drop on Farsight or AnyKey Café.

Do you want me to also make it as a PDF one-pager (like the stripped primer), so it’s forum-friendly and you don’t need to paste raw text?

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