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RAG and LoRa setup — Part 3

Conversation provenance

Source: ChatGPT data export · Conversation ID: 68ce16c7-b470-832c-9486-e5f1fa82efe3

Created: 2025-09-19T19:51:54-07:00 · Part 3 of 3

Darren2025-09-19T22:04:33-07:00

darren@ougway:~/ougway_env/llama.cpp$ /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 \
-p "You are Ougway. Greet the traveler briefly."
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) – 197 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)
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 5921.78 MiB on device 0: cudaMalloc failed: out of memory
alloc_tensor_range: failed to allocate CUDA0 buffer of size 6209437696
llama_model_load: error loading model: unable to allocate CUDA0 buffer
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model '/home/darren/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf', try reducing –n-gpu-layers if you're running out of VRAM
main: error: unable to load model
darren@ougway:~/ougway_env/llama.cpp$ Apparently not. It said it only went to 11.734, but it says out of memory, so I don't know.

ChatGPT2025-09-19T22:04:37-07:00 · gpt-5-thinking

Good news: nothing’s “wrong” with Q6_K. The problem is the GPU was already full before load.

See this line:

> using device ... - 197 MiB free

That means something else was still holding ~12 GB of VRAM (very likely your earlier Q8_0 session in another terminal). With only 197 MiB free, trying to allocate the ~5.9 GiB Q6_K model buffer will always OOM.

Do this:

1) See what’s on the GPU

nvidia-smi

Look under Processes. Note the PIDs using lots of memory (e.g., llama-cli, python, maybe a browser tab).

2) Kill the stragglers

kill &lt;PID1&gt; &lt;PID2&gt;
# if they don&#x27;t die quickly:
kill -9 &lt;PID1&gt; &lt;PID2&gt;

3) (Optional) Quick sweep for any leftover llama runs:

ps -ef | grep -i llama

4) Confirm VRAM is free

nvidia-smi

You should now see ~11–12 GiB free.

5) Retry the Q6_K 32k run

/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 \
  -p &quot;You are Ougway. Greet the traveler briefly.&quot;

If you still don’t have enough free VRAM (e.g., compositor/browser is heavy), you can buy a little headroom by moving a few layers to CPU just for the first run:

/home/darren/.../llama-cli \
  -m /home/darren/.../qwen3-8b-q6_k.gguf \
  -ngl 30 -c 32768 -b 8 -t $(nproc) -n 300 --jinja \
  -p &quot;You are Ougway. Greet the traveler briefly.&quot;

…but ideally, once the old session is closed, -ngl 999 at 32k should fit fine with Q6_K.

Darren2025-09-19T22:08:34-07:00

I closed that terminal window and started over … the process did not die and I did not feel like chasing them down …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 400 –jinja \
-p "You are Ougway. Greet the traveler briefly."
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) – 11590 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
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set –system-prompt (-sys) instead?
main: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant

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: interactive mode on.
sampler seed: 2747821454
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 = 16, n_predict = 400, n_keep = 0

== Running in interactive mode. ==
– Press Ctrl+C to interject at any time.
– Press Return to return control to the AI.
– To return control without starting a new line, end your input with '/'.
– If you want to submit another line, end your input with '\'.
– Not using system message. To change it, set a different value via -sys PROMPT

user
You are Ougway. Greet the traveler briefly.
assistant
<think>
Okay, the user wants me to respond as Ougway, the friendly robot from the Mario Kart series. They asked to greet the traveler briefly. Let me think about how Ougway speaks.

Ougway is known for his cheerful and enthusiastic personality. He often uses phrases like "Hey there!" and "Let's race!" to engage others. Since it's a brief greeting, I should keep it short and friendly. Maybe start with a cheerful exclamation, then offer a friendly challenge or invitation to race. I should make sure the tone is upbeat and positive. Let me check if there are any specific terms or phrases Ougway uses. He also mentions his love for racing, so including something about that would be good. Alright, putting it all together: a greeting, a mention of racing, and an invitation to join. Let me make sure it's concise and matches his character.
</think>

"Hey there! Ready to race? Let's go! 🏎️💨"

ChatGPT2025-09-19T22:08:36-07:00 · gpt-5

Perfect — it worked this time. 🎉

Notice the differences from the failed run:

  • - 11590 MiB free at startup (instead of ~197 MiB), so the model had plenty of room to allocate.
  • Q6_K offloaded all 36 layers + output head to the GPU (~5.9 GiB VRAM used).
  • KV cache for 32k context took ~4.6 GiB.
  • That leaves ~1 GiB headroom on your 12 GB 3060 — tight, but enough to run.

So yes — you’ve now got Qwen3-8B-Q6_K running at full 32k context cleanly.

👉 Next step is just stress-testing: feed it a large context (e.g., long docs + instructions) and confirm inference doesn’t OOM. But the load phase shows the VRAM math works out.

Do you want me to give you a quick VRAM breakdown table (model, KV cache, buffers) so you can see exactly how close to the ceiling you are?

Darren2025-09-19T22:09:15-07:00

Let's go ahead and do the stress test.

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

Got it 👍 A good stress test is to:

  1. Fill the context with thousands of tokens (dummy filler + some real text).
  2. Run a summarization / question task that forces the model to attend across the whole window.
  3. Watch VRAM usage and confirm it doesn’t OOM or slow to a crawl.

Here’s a ready-to-run command that should push your Qwen3-8B-Q6_K at 32k context: 

/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 --jinja \
  -p &quot;
--- START CONTEXT ---
$(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 30000)
--- 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.&quot;

🔍 What this does:
– yes ... | head -n 30000 generates ~30k lines of filler so you actually hit close to the 32k limit.
– The prompt ends with a real question that forces the model to look back.
– Output capped at 512 tokens (-n 512) so you don’t drown in text.

⚠️ While it runs, watch VRAM: 

watch -n 1 nvidia-smi

If it stays under ~11.8–11.9 GB, you’re golden.

Do you want me to also prep a cleaner dataset (like a mix of filler + varied technical text) so the stress test is more realistic than just repeating one line?

Darren2025-09-19T22:10:06-07:00

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 –jinja \
-p "
— START CONTEXT —
$(yes 'The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This is filler text for stress testing long contexts.' | head -n 30000)
— END CONTEXT —
Question: In 5 bullets, summarize the key ideas about symmetry and boundary conditions from the context above. Then list 3 questions that remain unanswered."
bash: /home/darren/ougway_env/llama.cpp/build/bin/llama-cli: Argument list too long
darren@ougway:~/Desktop$

ChatGPT2025-09-19T22:10:08-07:00 · gpt-5

Ah, yep — that’s the shell hitting its ARG_MAX limit (too many characters when the $() expands). 🛑

Two clean fixes:

Option 1 – Use a heredoc (simplest)

Put the filler into stdin instead of stuffing it into the -p argument:

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 30000 &gt; filler.txt

/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 --jinja &lt;&lt;&#x27;EOF&#x27;
--- START CONTEXT ---
$(cat filler.txt)
--- 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

But note: $() inside &lt;&lt;&#x27;EOF&#x27; won’t expand because of the quotes. Instead, just cat the filler before the heredoc.

Option 2 – Concatenate filler + question into a file

# Generate filler
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 30000 &gt; stress_context.txt

# Append the actual task
cat &lt;&lt;&#x27;EOF&#x27; &gt;&gt; stress_context.txt
--- 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

# Run model with -f instead of -p
/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 --jinja \
  -f stress_context.txt

👉 I recommend Option 2 — cleaner, avoids shell expansion limits, and lets you re-run the test just by editing stress_context.txt.

Want me to generate a mixed stress_context.txt for you (blend of filler + unique technical text) so the test isn’t just the same line repeated? That way, you’ll see if it really handles attention across variety, not just duplicates.

Darren2025-09-19T22:11:06-07:00

darren@ougway:~/Desktop$ yes 'The Flower Lattice primer discusses symmetry, frequency domains, and boundary conditions. This is filler text for stress testing long contexts.' | head -n 30000 > filler.txt

/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 –jinja <<'EOF'
— START CONTEXT —
$(cat filler.txt)
— 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
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) – 11552 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
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
main: chat template example:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant

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: interactive mode on.
sampler seed: 365720766
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 = 16, n_predict = 512, n_keep = 0

== Running in interactive mode. ==
– Press Ctrl+C to interject at any time.
– Press Return to return control to the AI.
– To return control without starting a new line, end your input with '/'.
– If you want to submit another line, end your input with '\'.
– Not using system message. To change it, set a different value via -sys PROMPT

> — START CONTEXT —
<think>
Okay, the user provided a context about a product named "FusionBlade" and a conversation between a customer and a sales representative. They want me to act as the sales rep and answer their question about the product's performance. The user's question is in Chinese, asking about the product's performance. I need to respond in Chinese, so first, I should make sure I understand the question correctly.

The context mentions that FusionBlade has a high-performance processor, advanced cooling system, and a user-friendly interface. It's designed for both casual and professional users. The sales rep's previous response highlighted these features. The user's question is asking about the performance, so I should elaborate on the processor, cooling, and any other relevant aspects that contribute to performance. Maybe also mention the user interface since it's part of the product's design. I should keep the response concise but informative, ensuring all key points are covered without being too technical. Let me structure the answer to first address the performance directly, then break down the components that support it, and finally mention the user interface and target audience. Make sure the tone is helpful and matches the sales rep's previous response style.
</think>

销售代表：
“FusionBlade 的性能表现非常出色，主要得益于其搭载的高性能处理器、先进的散热系统以及优化的软件架构。具体来说：

  1. 处理器性能：采用最新一代的多核处理器，支持超线程技术，能够轻松应对高强度计算任务（如视频剪辑、3D渲染、大型游戏等），同时保持低功耗运行。
  2. 散热系统：内置双风扇与液冷技术，确保在高负载下仍能维持稳定温度，避免性能降频，延长硬件寿命。
  3. 用户界面优化：简洁直观的操作系统界面与智能资源分配功能，让新手用户也能快速上手，而专业用户则可通过自定义设置进一步挖掘性能潜力。

无论是日常办公还是专业级应用，FusionBlade 都能提供流畅的体验。您有具体使用场景吗？我可以为您进一步分析是否匹配需求。”

说明：以上回答紧扣产品核心卖点（性能、散热、易用性），并主动引导用户补充需求，便于提供更精准的解决方案。$(cat filler.tx
t
)>
[New LWP 401863]
[New LWP 401865]
[New LWP 401866]
[New LWP 401867]
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
0x00007963caeea42f in __GI___wait4 (pid=401924, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory.

0 0x00007963caeea42f in __GI___wait4 (pid=401924, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30

30 in ../sysdeps/unix/sysv/linux/wait4.c

1 0x00007963cb57382b in ggml_print_backtrace () from /home/darren/ougway_env/llama.cpp/build/bin/libggml-base.so

2 0x00007963cb5861ef in ggml_uncaught_exception() () from /home/darren/ougway_env/llama.cpp/build/bin/libggml-base.so

3 0x00007963cb2ae20c in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6

4 0x00007963cb2ae277 in std::terminate() () from /lib/x86_64-linux-gnu/libstdc++.so.6

5 0x00007963cb2ae4d8 in __cxa_throw () from /lib/x86_64-linux-gnu/libstdc++.so.6

6 0x00007963cb2a54cd in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6

7 0x000055c1928407bc in common_chat_format_singleabi:cxx11 ()

8 0x000055c192796b45 in main::{lambda(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#2}::operator()(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const ()

9 0x000055c1927935af in main ()

[Inferior 1 (process 401859) detached]
terminate called after throwing an instance of 'std::out_of_range'
what(): basic_string::substr: __pos (which is 2461) > this->size() (which is 1239)
Aborted (core dumped)
darren@ougway:~/Desktop$

ChatGPT2025-09-19T22:11:08-07:00 · gpt-5-thinking

Yep—I see exactly what happened.

  • Your heredoc was single-quoted (&lt;&lt;&#x27;EOF&#x27;), so the shell did not expand $(cat filler.txt). That literal text went straight into the prompt.
  • With --jinja (chat template enabled), llama.cpp tried to wrap your giant raw prompt as a chat message, and hit a substring bug in the chat formatter → std::out_of_range … substr. That’s the crash you saw in the backtrace.

Let’s do a clean, bullet-proof stress test that avoids both issues:

Build the big prompt into a file (no shell expansion tricks)

# 1) Make the filler (~30k lines)
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 30000 &gt; filler.txt

# 2) Assemble the full prompt file
printf &#x27;--- START CONTEXT ---\n&#x27; &gt; stress_context.txt
cat filler.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

Run with no chat templating (avoids the jinja formatter path)

/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

That keeps everything simple: raw text in, raw answer out. If you want chat style, we can re-enable --jinja later with a smaller -p and keep the bulk in -f.

If anything still hiccups

  • Trim a little off the context (e.g., head -n 28000) to leave room for the template/answer.
  • Or keep 30k lines but drop -n to 256.
  • Make sure no other CUDA jobs are running (nvidia-smi should show ~11–12 GB free before load).

Want me to add a tiny script stress32k.sh that generates the files and runs the test in one go?

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