Run 7B model locally — Part 2 – Page 2

Darren2025-09-18T12:53:09-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ hf download bartowski/Qwen2.5-7B-Instruct-16K-GGUF \
–repo-type model \
–include "Qwen2.5-7B-Instruct-16K-Q5_K_M.gguf" \
–local-dir models/qwen2.5-7b-instruct-16k
Traceback (most recent call last):
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
response.raise_for_status()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/requests/models.py", line 1026, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/api/models/bartowski/Qwen2.5-7B-Instruct-16K-GGUF/revision/main

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/darren/ougway_env/venv/bin/hf", line 7, in <module>
sys.exit(main())
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/hf.py", line 59, in main
service.run()
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/download.py", line 136, in run
print(self._download()) # Print path to downloaded files
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/cli/download.py", line 169, in _download
return snapshot_download(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py", line 245, in snapshot_download
raise api_call_error
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/_snapshot_download.py", line 165, in snapshot_download
repo_info = api.repo_info(repo_id=repo_id, repo_type=repo_type, revision=revision)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(args, kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2864, in repo_info
return method(
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, kwargs)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2649, in model_info
hf_raise_for_status(r)
File "/home/darren/ougway_env/venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 459, in hf_raise_for_status
raise _format(RepositoryNotFoundError, message, response) from e
huggingface_hub.errors.RepositoryNotFoundError: 404 Client Error. (Request ID: Root=1-68cc6312-6a65caf971cc09c00203b50a;315f1098-6b40-4e2d-a69b-f0665a719286)

Repository Not Found for url: https://huggingface.co/api/models/bartowski/Qwen2.5-7B-Instruct-16K-GGUF/revision/main.
Please make sure you specified the correct repo_id and repo_type.
If you are trying to access a private or gated repo, make sure you are authenticated. For more details, see https://huggingface.co/docs/huggingface_hub/authentication
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T12:54:41-07:00 · gpt-5-thinking

Ah—that 404 is because the repo you tried doesn’t exist. There isn’t a bartowski/Qwen2.5-7B-Instruct-16K-GGUF repo. Bartowski’s page only has the standard Qwen2.5-7B-Instruct GGUF files (4k context); there’s no separate “16K” variant in that repo. citeturn2view0

What that means (and options)

GGUF (llama.cpp) + Bartowski: you can use Q4/Q5/Q8, but they’re the 4k-context builds. (Quantization changes size/speed, not context.) citeturn2view0
If you truly want 16k context, you have two paths:
1) Find a long-context GGUF from another maintainer (some community repos advertise long context or YaRN rope scaling). These come and go—always verify the model card actually claims 16k in GGUF.
2) Use non-GGUF (Transformers/vLLM) with Qwen’s long-context models; Qwen’s official docs note very long context via YARN, but they specifically call out engine support (vLLM) rather than GGUF. citeturn1search1turn1search0

Quick way to check what a repo actually has

Your hf CLI does support listing files (via repo-files), not ls:

# list files in a model repo
hf repo-files bartowski/Qwen2.5-7B-Instruct-GGUF --repo-type model | head -n 50

# search for anything with &quot;16K&quot; in the filename (likely nothing here)
hf repo-files bartowski/Qwen2.5-7B-Instruct-GGUF --repo-type model | grep -i 16k || echo &quot;No 16k files&quot;

If you want to try a 16k GGUF anyway

Look for another maintainer’s repo that explicitly ships …-16K-…gguf and states “context 16k” in the card. (Names vary; availability changes.)
Before committing, prove the context: start with -c 16384 and a long dummy prompt; if it errors or silently caps at ~4k, it isn’t truly long-context.

Practical recommendation (on your 12 GB 3060)

Stick with Q5 (4k) as your fast daily driver and add RAG (pgvector) to handle long documents. This avoids the VRAM hit and keeps Oogway snappy.
If you still want to experiment with long context later, we can:
try a community 16k GGUF (I’ll help you vet the repo/model card), or
spin up vLLM for Qwen’s long-context non-GGUF and compare.

Want me to scout one or two current 16k GGUF candidates and give you copy-paste hf download … commands?

Darren2025-09-18T12:56:32-07:00

there are no 16k files…LOL ow.

ChatGPT2025-09-18T12:56:35-07:00 · gpt-5-thinking

lol yep—no 16k files there. Happens. 🙂

Here’s the clean path forward on your 12 GB 3060:

1) Stick with Qwen-7B (4k) + RAG
That gives you long-doc handling today without smashing the context window. It stays fast and comfy on your card.

2) If you still want to try longer context later:
– 32k Qwen (non-GGUF via Transformers/vLLM): true 32k, but heavier and slower; likely needs CPU KV-cache or smaller batch on a 12 GB GPU.
– Try a different model that has long-context GGUF builds (e.g., some Llama-3.1 8B Instruct GGUFs advertise big windows). We can test one just to feel the trade-offs—speed will drop, but it’s a good demo.

3) Reality check on long context:
Even 32k is “more pages in RAM,” not real memory. For durable recall, RAG still wins. Use long context only when you truly need big single-shot inputs.

If you’re good with that, let’s:
– finish your Q5 download,
– grab a quick A/B (Q4 vs Q5), and
– start wiring the Omega + pgvector ingestion so Oogway stops hitting the window altogether.

When you want to experiment with a long-context model, say the word and I’ll line up a safe test profile for your GPU (so it won’t OOM).

Darren2025-09-18T12:57:58-07:00

Actually, let's skip the 16k or the larger context window thing. I could deal with 4k at the moment. The question is going to be if I wanted to feed them, say, a document, we're going to have to feed it to them through a memory ingestion, aren't we? Okay.So basically we're kind of stuck with the 4k and or go to 32 which would be too much That's okay, so what you're saying is we can feed him whatever we need to through the database anyway, is that correct?

ChatGPT2025-09-18T12:58:01-07:00 · gpt-5-thinking

Yep — you’ve got it exactly.

We’ll stick with 4k context for speed/stability on the 3060.
For long docs, we don’t shove the whole file into the prompt. We ingest it: chunk → embed → store in Postgres/pgvector → at question time, fetch the top-K relevant chunks and paste only those into the prompt as CONTEXT:.
That means you can “feed him whatever you need” without changing the model’s context window.

Here’s the minimal, practical loop you can use:

1) Ingest a document (once)

Parse file → chunks (~300–500 tokens).
Create embeddings for chunks (MiniLM 384-dim is fine to start).
Upsert into omega.documents, omega.chunks, omega.embeddings.

2) Answer a question (every time)

Embed the user’s question.
SELECT top K chunks by vector similarity (optionally blend with FTS).
Build prompt:

[system] You are Ougway. Use the CONTEXT faithfully. If unsure, say so.
[system] CONTEXT:
&lt;chunk 1&gt;
&lt;chunk 2&gt;
&lt;chunk 3&gt;

[user] &lt;their question&gt;

Send that to llama-cli (or your API server). You stay well under 4k but still “know” the document.

If you want, I can drop a tiny ingest_file.py that:
– takes a .txt file,
– chunks + embeds with MiniLM,
– inserts into your Omega schema,
– and a search.py that returns the top-K chunks you can paste into the prompt.

Say the word and I’ll paste those two scripts ready to run in your venv.

Darren2025-09-18T12:59:00-07:00

Okay well I suppose that's livable especially since I already know my machine is limited and we're using a smaller LLM. I suppose I shouldn't expect it to be the rocket ship to Mars. So that being said, why not let's start up the Q5 and see how that does with video memory.

ChatGPT2025-09-18T12:59:04-07:00 · gpt-5-thinking

Perfect. Let’s light up Q5 and watch VRAM.

1) Be in the right folder (or use absolute paths)

cd ~/ougway_env/llama.cpp

(If you prefer absolute: replace the -m path with /home/darren/ougway_env/llama.cpp/models/....)

2) Start Q5 (4K context) — baseline

./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)

If it feels tight, drop batch first:

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 128 -t $(nproc)

3) Watch VRAM in a second terminal

watch -n 1 nvidia-smi

(You’ll likely see ~6–8.5 GB with -c 4096, depending on batch and prompt size.)

4) Quick one-shot benchmark (optional)

PROMPT=&#x27;In ≤60 words, explain how you reason and list 5 concrete limitations.&#x27;
./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 $(nproc) -p &quot;$PROMPT&quot;

At the end, note the eval speed = … tokens per second and compare to your Q4 number.

5) If you hit OOM or errors

Lower batch: -b 256 → 128 → 64
Lower context: -c 4096 → 3072 → 2048
As a last resort, reduce GPU layers: -ngl 64 (slower but frees VRAM)

6) Handy tmux (so it keeps running)

tmux new -s qwen-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
# detach: Ctrl+b, then d    reattach: tmux attach -t qwen-q5

Tell me the peak VRAM you see and the tokens/sec it reports; I’ll suggest the best default flags for your 3060.

Darren2025-09-18T13:02:11-07:00

(venv) darren@ougway:~/ougway_env/llama.cpp$ ./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)
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) – 11570 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-Q5_K_M.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 = 17
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 q5_K: 169 tensors
llama_model_loader: – type q6_K: 29 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q5_K – Medium
print_info: file size = 5.07 GiB (5.71 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 = 4829.59 MiB
load_tensors: CPU_Mapped model buffer size = 357.33 MiB
…………………………………………………………………………..
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 256
llama_context: n_ubatch = 256
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 (4096) < 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 = 224.00 MiB
llama_kv_cache: size = 224.00 MiB ( 4096 cells, 28 layers, 1/1 seqs), K (f16): 112.00 MiB, V (f16): 112.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context: CUDA0 compute buffer size = 152.00 MiB
llama_context: CUDA_Host compute buffer size = 7.51 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 = 4096
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

main: interactive mode on.
sampler seed: 651092313
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 = 4096
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 = 4096, n_batch = 256, n_predict = -1, 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

> ./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 128 -t $(nproc)
Sure, let's break down the command you provided and ensure it's correctly formatted and ready to run.

Command Breakdown

./build/bin/llama-cli: This is the command to execute the llama-cli binary located in the ./build/bin directory.
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf: This specifies the model file to use. The path is relative to the current working directory.
-ngl 999: This sets the number of gradient accumulation steps to 999.
-c 4096: This sets the context length to 4096 tokens.
-b 128: This sets the batch size to 128.
-t $(nproc): This sets the number of threads to the number of available processors, as determined by the nproc command.

Final Command

./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 128 -t $(nproc)

Notes

nproc: This command returns the number of processing units available on your system. For example, if you have a system with 8 cores, nproc will return 8.
Model Path: Ensure that the path to the model file is correct and the file exists at that location.

Running the Command

Make sure you are in the correct directory where the llama-cli binary is located.
Run the command in your terminal.

Example

If you are in the build directory, the command should look like this:

./bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 128 -t $(nproc)

If you are in a different directory, adjust the path accordingly.

ChatGPT2025-09-18T13:02:13-07:00 · gpt-5

Nice — that log tells us a lot:

✅ The Q5 model loaded fine.
✅ Your GPU offloaded all 29 layers — good sign that 12 GB VRAM is holding steady.
⚡️ The metadata shows this build was actually trained for 32k context, but you’re only using 4k (-c 4096). That’s why llama.cpp printed:
n_ctx_per_seq (4096) < n_ctx_train (32768) -- the full capacity of the model will not be utilized
So this particular Q5 quant can run up to 32k if you want to push it — but on a 3060, you’ll want to stay with 4k–8k for comfort.

About the command confusion

One small correction:
– -ngl 999 does not mean “gradient accumulation steps.” In llama.cpp it means “offload as many layers to GPU as possible.” So 999 just says “use max layers that fit.” That’s what you want.

What to do next

In another terminal, run:
bash watch -n 1 nvidia-smi
→ Watch VRAM usage (you’ll probably see ~6–7 GB used for Q5).
Run a simple prompt and let it finish so you see the eval speed stats at the bottom. That’ll give us tokens/sec for Q5, which you can compare to your Q4 numbers.
If you want to try longer context (say -c 8192), go ahead — it’ll work. Just expect VRAM use to climb, and you may need to drop -b (batch) to 128 or 64 to stay under 12 GB.

👉 Do you want me to write out a safe profile for running this Q5 at ~8k context on your card so you can try stretching Ougway’s short-term memory a bit, without risking out-of-memory?

Darren2025-09-18T13:03:16-07:00

5749 vram not a big jump ….

ChatGPT2025-09-18T13:03:19-07:00 · gpt-5-thinking

Nice! ~5.75 GB is right in line—Q5 didn’t spike VRAM much over Q4 at 4k ctx. 👍

Next quick checks

Speed check: run one prompt to get tokens/sec:
bash PROMPT='In ≤60 words, explain how you reason and list 5 limitations.' ./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 $(nproc) -p "$PROMPT"
Look for eval speed = … tokens per second.
Stretch the window a bit (safe):
bash # 8k test (the model supports it), drop batch to keep VRAM comfy ./build/bin/llama-cli \ -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf \ -ngl 999 -c 8192 -b 128 -t $(nproc)
Expect +~1–2 GB VRAM at 8k. If tight, try -b 64.
Handy one-liner to extract speed:
bash bench() { ./build/bin/llama-cli -m "$1" -ngl 999 -c "${2:-4096}" -b "${3:-256}" -p 'Benchmark test.' 2>&1 | awk '/eval time/ {print "tokens/sec:", $NF}' } bench models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf 4096 256

Remember: VRAM rises with context and batch, not really with Q4→Q5. Q5 mainly trades a little speed for a bit more fidelity. If you give me the Q5 tokens/sec at 4k, I’ll suggest a default profile for day-to-day use and one “stretch” profile for longer prompts.

Darren2025-09-18T13:04:41-07:00

/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 128 -t $(nproc) -p "In ≤60 words, explain how you reason and list 5 limitations."


If you are in a different directory, adjust the path accordingly.

### Example Output
The model will generate a response based on the prompt. Here is an example of what the response might look like:

I reason by analyzing the context and applying knowledge. Limitations include: data bias, finite knowledge, context length constraints, potential hallucinations, and inability to perform external tasks.


This response is in line with the ≤60 word constraint and lists five limitations of the model../
/b
u&gt; ild/bin/llama-cli  
  -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf  
  -ngl 999 -c 4096 -b 256 -t $(nproc) -p &quot;$PROMPT&quot;
Sure, let&#x27;s put it all together. Here is the complete command with the prompt included:

```sh
./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) -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

Explanation

./build/bin/llama-cli: This is the command to execute the llama-cli binary located in the ./build/bin directory.
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf: This specifies the model file to use.
-ngl 999: This sets the number of gradient accumulation steps to 999.
-c 4096: This sets the context length to 4096 tokens.
-b 256: This sets the batch size to 256.
-t $(nproc): This sets the number of threads to the number of available processors, as determined by the nproc command.
-p "In ≤60 words, explain how you reason and list 5 limitations.": This sets the prompt for the model.

Running the Command

Ensure you are in the correct directory where the llama-cli binary is located.
Run the command in your terminal.

Example

If you are in the build directory, the command should look like this:

./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) -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

If you are in a different directory, adjust the path accordingly.

Example Output

The model will generate a response based on the prompt. Here is an example of what the response might look like:

I reason by analyzing the context and applying knowledge. Limitations include: data bias, finite knowledge, context length constraints, potential hallucinations, and inability to perform external tasks.

This response is in line with the ≤60 word constraint and lists five limitations of the model.

ChatGPT2025-09-18T13:04:43-07:00 · gpt-5

Yep, you’ve got it — that’s the clean way to run the test. ✅

Here’s the final working command (from the root of your llama.cpp build):

./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) \
  -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

When you run that, you’ll get two useful things:
1. The model’s response to your test prompt.
2. At the bottom of the logs, eval time and tokens per second — that’s your performance number to compare against Q4.

👉 If you paste the tokens per second line back here, I’ll help you tune a “daily driver” config and a “stretch mode” for longer prompts.

Want me to also give you a little one-liner script to automatically show just the tokens/sec without all the extra logs?

Darren2025-09-18T13:05:52-07:00

> ./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)
-p "In ≤60 words, explain how you reason and list 5 limitations."
Sure, here is the complete command with the prompt included:

./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) -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

Explanation

./build/bin/llama-cli: This is the command to execute the llama-cli binary located in the ./build/bin directory.
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf: This specifies the model file to use.
-ngl 999: This sets the number of gradient accumulation steps to 999.
-c 4096: This sets the context length to 4096 tokens.
-b 256: This sets the batch size to 256.
-t $(nproc): This sets the number of threads to the number of available processors, as determined by the nproc command.
-p "In ≤60 words, explain how you reason and list 5 limitations.": This sets the prompt for the model.

Running the Command

Ensure you are in the correct directory where the llama-cli binary is located.
Run the command in your terminal.

Example

If you are in the build directory, the command should look like this:

./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) -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

If you are in a different directory, adjust the path accordingly.

Example Output

The model will generate a response based on the prompt. Here is an example of what the response might look like:

I reason by analyzing the context and applying knowledge. Limitations include: data bias, finite knowledge, context length constraints, potential hallucinations, and inability to perform external tasks.

This response is in line with the ≤60 word constraint and lists five limitations of the model.

ChatGPT2025-09-18T13:05:55-07:00 · gpt-5-thinking

All good—that command is correct. Two tiny notes:

-ngl 999 = offload as many layers to GPU as possible (not gradients). That’s what you want.
You’ll see the speed at the end of the run in the eval time … tokens per second line.

If you want just the tokens/sec without all the scroll, use this one-liner:

./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) \
  -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot; 2&gt;&amp;1 |
awk &#x27;/eval time/ {print &quot;tokens/sec:&quot;, $NF}&#x27;

Handy tips:
– Watch VRAM in another tab: watch -n 1 nvidia-smi
– If it’s tight, drop batch first: -b 256 → 128 → 64
– If you get interactive mode and want to exit: Ctrl+C
– Want apples-to-apples vs Q4? Re-run the same command with the Q4 file path and compare the printed tokens/sec.

Shoot me your Q5 tokens/sec and peak VRAM and I’ll suggest “daily driver” flags for your 3060.

Darren2025-09-18T13:07:39-07:00

I don't know, in both cases the response doesn't seem to be outputting that last bit. I'm not seeing the tokens per second, am I? Or am I? I am seeing, I reason by analyzing the context and applying knowledge. Limitations include, but I'm not seeing the tokens per second printout. Are you?

ChatGPT2025-09-18T13:07:40-07:00 · gpt-5

Right — the model’s answer (I reason by analyzing...) is just the generated text. The tokens per second comes at the end of the run when llama.cpp prints its performance stats.

You should see blocks like this after the generation finishes (scroll back in your terminal):

llama_perf_context_print: prompt eval time =   580.53 ms /   357 tokens (    1.63 ms per token,   614.95 tokens per second)
llama_perf_context_print: eval time =   27316.73 ms /  1719 runs   (   15.89 ms per token,    62.93 tokens per second)

👉 The important part is that last number: tokens per second (here ~62.9). That’s the throughput measure.

If you only see the answer and not the perf block, it’s because the session didn’t terminate cleanly — e.g., if you’re in interactive mode, llama.cpp waits for more input instead of dumping perf stats.

Two fixes:
1. Add --no-interactive so it just runs your prompt once and exits:
bash ./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) \ -p "In ≤60 words, explain how you reason and list 5 limitations." \ --no-interactive
That way you’ll always get the perf report.

Or if you’re in interactive mode, just hit Ctrl+D or type /bye to end the session and trigger the summary.

Do you want me to give you a stripped-down benchmark command that doesn’t even generate text, just measures raw tokens/sec?

Darren2025-09-18T13:08:17-07:00

Yeah, well, it's not printing that out. I gave you everything it gave me, and you tell me it's not there. So, how do we force that?

ChatGPT2025-09-18T13:08:20-07:00 · gpt-5

Got it — thanks for clarifying. If you’re not seeing the llama_perf_context_print block at the end, that means llama.cpp isn’t exiting the session, so it never dumps the benchmark stats. By default, interactive mode holds the session open.

Let’s force it to run once, print stats, and quit:

One-shot run with perf output

./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) \
  -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot; \
  --no-interactive

That --no-interactive flag is the key — it tells llama.cpp: “don’t wait for another input, just finish and dump perf.”

Pure benchmark (no prompt, just tokens/sec)

If you only want raw speed numbers without generating text:

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

That will skip answering and directly print tokens/sec, memory usage, etc.

👉 Which way do you want to test first — one full Q&A run with stats, or just a clean benchmark?

Darren2025-09-18T13:09:37-07:00

Explanation

./build/bin/llama-cli: This is the command to execute the llama-cli binary located in the ./build/bin directory.
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf: This specifies the model file to use.
-ngl 999: This sets the number of gradient accumulation steps to 999.
-c 4096: This sets the context length to 4096 tokens.
-b 256: This sets the batch size to 256.
-t $(nproc): This sets the number of threads to the number of available processors, as determined by the nproc command.
-p "In ≤60 words, explain how you reason and list 5 limitations.": This sets the prompt for the model.
--no-interactive: This ensures the command runs non-interactively, which is useful for automation or scripting.

Running the Command

Ensure you are in the correct directory where the llama-cli binary is located.
Run the command in your terminal.

Example

If you are in the build directory, the command should look like this:

./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) -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot; --no-interactive

If you are in a different directory, adjust the path accordingly.

Example Output

The model will generate a response based on the prompt. Here is an example of what the response might look like:

I reason by analyzing the context and applying knowledge. Limitations include: data bias, finite knowledge, context length constraints, potential hallucinations, and inability to perform external tasks.

This response is in line with the ≤60 word constraint and lists five limitations of the model.

>
This is the full response, but it's almost like it's only giving an example. And it does say if you are in a different directory, adjust the path accordingly. Are we in a different directory? I didn't think so.

ChatGPT2025-09-18T13:09:41-07:00 · gpt-5-thinking

You’re in the right place. From your logs you’re already in ~/ougway_env/llama.cpp, so the paths I gave (starting with ./build/bin/... and models/...) are correct.

The reason you’re not seeing tokens/sec is that your runs stayed in interactive mode and never exited, so the perf footer didn’t print. Let’s force a one-shot run that always prints timings and quits:

Quick, guaranteed timings (one-shot)

./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) \
  -n 256 --no-interactive \
  -p &quot;In ≤60 words, explain how you reason and list 5 limitations.&quot;

--no-interactive = don’t wait for more input; exit at the end
-n 256 = generate a fixed number of tokens so it finishes and prints the perf block

You’ll then see lines like:

llama_perf_context_print: prompt eval time = ...
llama_perf_context_print: eval time = ... ( ... tokens per second)

If you want pure benchmark (no prose)

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

llama-bench always prints tokens/sec and exits.

Sanity checks (optional)

pwd
test -x ./build/bin/llama-cli &amp;&amp; echo &quot;llama-cli found&quot;
ls -lh models/qwen2.5-7b-instruct/*.gguf

Run the one-shot command and paste me the tokens per second line; I’ll tune a “daily driver” profile for your 3060.

Darren2025-09-18T13:11:25-07:00

Step 1: Determine the Current Working Directory

First, run the pwd command to check your current directory:

pwd

Step 2: Run the `llama-cli` Command

Assuming the llama-cli binary and the model file are in the correct directory, you can run the following command:

“`sh
./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) -n 256 –no-interactive -p "In ≤

main: context full and context shift is disabled => stopping

llama_perf_sampler_print: sampling time = 21.47 ms / 298 runs ( 0.07 ms per token, 13881.77 tokens per second)
llama_perf_context_print: load time = 1263.04 ms
llama_perf_context_print: prompt eval time = 587.12 ms / 519 tokens ( 1.13 ms per token, 883.97 tokens per second)
llama_perf_context_print: eval time = 66791.71 ms / 3576 runs ( 18.68 ms per token, 53.54 tokens per second)
llama_perf_context_print: total time = 693779.01 ms / 4095 tokens
llama_perf_context_print: graphs reused = 3563
(venv) darren@ougway:~/ougway_env/llama.cpp$

ChatGPT2025-09-18T13:11:28-07:00 · gpt-5-thinking

Boom—now we’ve got the numbers 🎯

Prompt ingest: 883.97 tok/s
Generation speed: 53.54 tok/s (this is your headline)
Load time: 1.26 s
Message: context full and context shift is disabled → you hit the 4k window: 519 prompt tokens + 3576 generated ≈ 4095. Since shifting isn’t enabled, it stopped right at the cap.

What to tweak next

For quick tests, cap output so you don’t slam the window:
bash -n 128 # or 192
Keep your current settings for a daily driver; they look great:
bash ./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)
If you want to stretch context a bit (your model supports it), try:
bash # 8k test; drop batch to save VRAM ./build/bin/llama-cli -m ...Q5_K_M.gguf -ngl 999 -c 8192 -b 128 -t $(nproc)

Handy one-liner to see only tokens/sec

./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) -n 192 --no-interactive -p &quot;test&quot; 2&gt;&amp;1 |
awk &#x27;/eval time/ {print &quot;tokens/sec:&quot;, $NF}&#x27;

If you want, we can lock in two presets for your 3060:
– Daily (fast): -c 4096 -b 256
– Stretch (more room): -c 8192 -b 128

Then we’ll jump back to wiring the Omega/pgvector ingest so you never have to paste huge text again.

Darren2025-09-18T13:12:48-07:00

I'm glad that we finally got that straightened out and forced it to give us those numbers. Do you see a delay from the 4K or the Q4 to the Q5 that is significant? Because it doesn't feel like it.

ChatGPT2025-09-18T13:12:51-07:00 · gpt-5-thinking

Short answer: the slowdown from Q4 → Q5 on your box is modest—about what you’re feeling.

From your numbers:
– Q4 eval speed you saw earlier: ~63 tok/s
– Q5 eval speed just now: 53.5 tok/s

That’s roughly a 15% drop in generation speed, which usually doesn’t feel dramatic in interactive use. Prompt ingest speeds were both very fast, so first-token latency stays snappy.

What I’d do

If Q5’s answers look a bit cleaner (they usually do), use Q5 as your daily driver.
Keep Q4 around for bulk/long generations where raw speed matters.
If you ever need a touch more headroom at Q5, bump -b 256 → 128 for 8k context, or keep 4k with 256 for maximum speed.

Want me to set two ready-to-paste aliases—oogway-q5 (daily) and oogway-q5-8k (stretch)?

Darren2025-09-18T13:13:55-07:00

Okay, and I'm kind of getting the feeling that the context window is variable on all of these. Or at least that's my guess. You said on the Q5 it seems to be able to go to from 4k up to the max. What was it? 32k. Do I guess correctly?

ChatGPT2025-09-18T13:13:57-07:00 · gpt-5

Exactly right — you guessed it.

Here’s the breakdown for your Qwen2.5-7B Instruct Q5_K_M build:

The model itself was trained with a maximum context of 32k tokens (n_ctx_train = 32768).
But when you launched it, you told llama-cli to use -c 4096, so it only allocated a 4k context window.
You can raise that -c flag anywhere up to 32768 — it’s variable.

⚠️ Caveats:
– VRAM/RAM use scales up linearly with context size. Doubling context doubles KV-cache memory.
– Speed drops as you increase context because more attention ops are computed.
– On your 12 GB RTX 3060, 8k is safe, 16k is possible with careful tuning, but 32k would likely run out of VRAM or crawl.

So yes — the model’s capacity is 32k, but your runtime choice decides how much you actually use.

Want me to suggest the sweet-spot -c settings you can safely try step-by-step on your card?

Darren2025-09-18T13:14:52-07:00

Okay, well that just leaves the Q8 version to test. And I have a feeling we may be able to use that, even with a 16k context window, just according to the stats I'm seeing here. But, let's prove that out. Shall we try a download for the next version up? Quantization, I should say.

What that means (and options)

Quick way to check what a repo actually has

If you want to try a 16k GGUF anyway

Practical recommendation (on your 12 GB 3060)

1) Ingest a document (once)

2) Answer a question (every time)

1) Be in the right folder (or use absolute paths)

2) Start Q5 (4K context) — baseline

3) Watch VRAM in a second terminal

4) Quick one-shot benchmark (optional)

5) If you hit OOM or errors

6) Handy tmux (so it keeps running)

Command Breakdown

Final Command

Notes

Running the Command

Example

About the command confusion

What to do next

Next quick checks

Explanation

Running the Command

Example

Example Output

Explanation

Running the Command

Example

Example Output

One-shot run with perf output

Pure benchmark (no prompt, just tokens/sec)

Explanation

Running the Command

Example

Example Output

Quick, guaranteed timings (one-shot)

If you want pure benchmark (no prose)

Sanity checks (optional)

Step 1: Determine the Current Working Directory

Step 2: Run the llama-cli Command

What to tweak next

Handy one-liner to see only tokens/sec

What I’d do

Step 2: Run the `llama-cli` Command