Conversation provenance
Source: ChatGPT data export · Conversation ID: 68cb703d-40a8-8325-9213-bb1b7f803af1
Created: 2025-09-17T19:36:46-07:00 · Part 3 of 3
Okay, what am I doing wrong? I'm running the commands that you gave me for the 4k sanity run And the one-shot benchmark and basically it's giving me instructions saying Oh, if you wanted to run this against the model, you need to do this just like last time. Am I not in the model yet?
) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -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) – 11601 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 339 tensors from models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen2
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen2.5 7B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen2.5
llama_model_loader: – kv 5: general.size_label str = 7B
llama_model_loader: – kv 6: general.license str = apache-2.0
llama_model_loader: – kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7…
llama_model_loader: – kv 8: general.base_model.count u32 = 1
llama_model_loader: – kv 9: general.base_model.0.name str = Qwen2.5 7B
llama_model_loader: – kv 10: general.base_model.0.organization str = Qwen
llama_model_loader: – kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-7B
llama_model_loader: – kv 12: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: – kv 13: general.languages arr[str,1] = ["en"]
llama_model_loader: – kv 14: qwen2.block_count u32 = 28
llama_model_loader: – kv 15: qwen2.context_length u32 = 32768
llama_model_loader: – kv 16: qwen2.embedding_length u32 = 3584
llama_model_loader: – kv 17: qwen2.feed_forward_length u32 = 18944
llama_model_loader: – kv 18: qwen2.attention.head_count u32 = 28
llama_model_loader: – kv 19: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: – kv 20: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 22: general.file_type u32 = 7
llama_model_loader: – kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 28: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 30: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 33: general.quantization_version u32 = 2
llama_model_loader: – kv 34: quantize.imatrix.file str = /models_out/Qwen2.5-7B-Instruct-GGUF/…
llama_model_loader: – kv 35: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: – kv 36: quantize.imatrix.entries_count i32 = 196
llama_model_loader: – kv 37: quantize.imatrix.chunks_count i32 = 128
llama_model_loader: – type f32: 141 tensors
llama_model_loader: – type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 7.54 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 3584
print_info: n_layer = 28
print_info: n_head = 28
print_info: n_head_kv = 4
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 7
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 18944
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: model type = 7B
print_info: model params = 7.62 B
print_info: general.name = Qwen2.5 7B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 152064
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 28 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CUDA0 model buffer size = 7165.44 MiB
load_tensors: CPU_Mapped model buffer size = 552.23 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 128
llama_context: n_ubatch = 128
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 = 76.00 MiB
llama_context: CUDA_Host compute buffer size = 3.75 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
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: 2382789592
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 = 128, 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-Q8_0.gguf
-ngl 999 -c 4096 -b 128 -t $(nproc)
Sure, it looks like you're running a command to interact with a large language model using the llama-cli tool. Here's a breakdown of the command:
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 \
-c 4096 \
-b 128 \
-t $(nproc)
./build/bin/llama-cli: The path to thellama-cliexecutable.-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf: The path to the model file.-ngl 999: Maximum number of gradients to load into memory. Setting this to 999 means it will load as much as possible.-c 4096: Maximum context length for input.-b 128: Batch size for inference.-t $(nproc): Number of threads to use, which is determined by the number of physical cores available on the system using thenproccommand.
This command is configured to use the specified model with a large context length and batch size, and it will utilize all available cores on your system for parallel processing.
Make sure that the model file Qwen2.5-7B-Instruct-Q8_0.gguf is correctly placed in the specified directory and that llama-cli is correctly built and installed. Also, ensure that your system meets the requirements for running the model (memory, CPU cores, etc.).
> ./build/bin/llama-cli
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf
-ngl 999 -c 4096 -b 128 -t $(nproc)
-n 192 –no-interactive
-p "In ≤60 words, explain how you reason and list 5 limitations."
Certainly! Your command is configured to run the llama-cli tool with specific parameters and provide a prompt for the model to respond to. Here's the complete command with the additional parameters:
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 --no-interactive \
-p "In ≤60 words, explain how you reason and list 5 limitations."
Let's break down the additional parameters:
-n 192: Maximum number of tokens to generate.--no-interactive: Disables the interactive mode and runs the command in a non-interactive mode.-p "In ≤60 words, explain how you reason and list 5 limitations.": The prompt to which the model will generate a response.
This command will run the model and generate a response to the provided prompt, reasoning in ≤60 words and listing 5 limitations, using up to 192 tokens.
Ensure that the model file Qwen2.5-7B-Instruct-Q8_0.gguf is correctly located at the specified path and that llama-cli is correctly built and installed. Also, verify that your system meets the required resources (memory, CPU cores, etc.).
Here's the command again for clarity:
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 --no-interactive \
-p "In ≤60 words, explain how you reason and list 5 limitations."
Run this command in your terminal to see the model's response.
>
You did nothing “wrong”—you just slipped into the model’s REPL (interactive mode).
When you see:
== Running in interactive mode. ==
>
anything you type after the > is given to the model as text, not executed by your shell. That’s why it replied with “Here’s a breakdown of the command…” — it was answering your message, not running a command.
Here’s how to get the Q8 benchmarks cleanly:
1) Quit the current interactive session
Press Ctrl+C once (or twice) until you’re back at your normal bash prompt darren@…$.
2) Run a one-shot benchmark (prints tokens/sec and exits)
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 --no-interactive \
-p "In <=60 words, explain how you reason and list 5 limitations."
Tip: I used <= instead of the fancy ≤ symbol to avoid any weird clipboard issues.
You should now always see the perf footer, including:
llama_perf_context_print: eval time = ... ( ... tokens per second)
3) If you only want raw speed (no text)
./build/bin/llama-bench \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc)
4) Quick “just show tokens/sec” helper
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) -n 192 --no-interactive \
-p "test" 2>&1 | awk '/eval time/ {print "tokens/sec:", $NF}'
Why it happened
- The first command (no
-p/--no-interactive) launched the REPL. - Then you pasted another command at the REPL prompt
>, so the model “answered” it like a normal chat.
Run the one-shot command above and tell me the tokens/sec (and peak VRAM from nvidia-smi). If that looks good, we’ll try 16k safely next.
(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 –no-interactive \
-p "In <=60 words, explain how you reason and list 5 limitations."
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
error: invalid argument: –no-interactive
(venv) darren@ougway:~/ougway_env/llama.cpp$
okay now it's telling me there's no interactive mode good lord
Ah — that explains it.
llama.cpp renamed a bunch of CLI flags in the last few months. Your build doesn’t recognize --no-interactive.
On your version, the flag to disable the REPL is simply -i 0.
(Short for “interactive = 0”).
So try this instead:
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 -i 0 \
-p "In <=60 words, explain how you reason and list 5 limitations."
That should:
– Load the Q8 model,
– Run your prompt once,
– Print the output,
– Exit with the perf stats (tokens/sec, VRAM, etc).
Want me to also show you how to always see the available flags (--help) for your build, so we don’t hit these version mismatches again?
I don't know. It didn't seem to like that either.
(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-n 192 -i 0 \
-p "In <=60 words, explain how you reason and list 5 limitations."
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
error: invalid argument: 0
(venv) darren@ougway:~/ougway_env/llama.cpp$
You didn’t do anything wrong — it’s just flag semantics on your build.
On your version:
-iis a flag (no value). Writing-i 0makes0an unexpected extra arg → henceinvalid argument: 0.--no-interactiveisn’t supported on this build.- Your run flipped into “conversation mode” automatically; the log even said: “chat template is available… (disable it with -no-cnv)”.
Do one of these:
A) One-shot run (prints timings and exits)
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-no-cnv -n 192 \
-p "In <=60 words, explain how you reason and list 5 limitations."
Notes:
– Don’t include -i at all.
– -no-cnv stops the auto “chat/REPL” behavior.
– -n 192 guarantees it finishes and prints the perf footer.
B) Pure benchmark (no text, always prints tokens/sec)
./build/bin/llama-bench \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc)
C) Quick “just show tokens/sec” filter
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) -no-cnv -n 192 -p "test" 2>&1 |
awk '/eval time/ {print "tokens/sec:", $NF}'
(Optional) See your exact flags
./build/bin/llama-cli -h | sed -n '1,160p'
Run A or B and you should finally see the eval time ... ( ... tokens per second) line.
Share that tokens/sec + peak VRAM and we’ll lock in your Q8 “keep or skip” decision and a safe 16k test profile.
Bingo!
(venv) darren@ougway:~/ougway_env/llama.cpp$ ./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 128 -t $(nproc) \
-no-cnv -n 192 \
-p "In <=60 words, explain how you reason and list 5 limitations."
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) – 11611 MiB free
llama_model_loader: loaded meta data with 38 key-value pairs and 339 tensors from models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = qwen2
llama_model_loader: – kv 1: general.type str = model
llama_model_loader: – kv 2: general.name str = Qwen2.5 7B Instruct
llama_model_loader: – kv 3: general.finetune str = Instruct
llama_model_loader: – kv 4: general.basename str = Qwen2.5
llama_model_loader: – kv 5: general.size_label str = 7B
llama_model_loader: – kv 6: general.license str = apache-2.0
llama_model_loader: – kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen2.5-7…
llama_model_loader: – kv 8: general.base_model.count u32 = 1
llama_model_loader: – kv 9: general.base_model.0.name str = Qwen2.5 7B
llama_model_loader: – kv 10: general.base_model.0.organization str = Qwen
llama_model_loader: – kv 11: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen2.5-7B
llama_model_loader: – kv 12: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: – kv 13: general.languages arr[str,1] = ["en"]
llama_model_loader: – kv 14: qwen2.block_count u32 = 28
llama_model_loader: – kv 15: qwen2.context_length u32 = 32768
llama_model_loader: – kv 16: qwen2.embedding_length u32 = 3584
llama_model_loader: – kv 17: qwen2.feed_forward_length u32 = 18944
llama_model_loader: – kv 18: qwen2.attention.head_count u32 = 28
llama_model_loader: – kv 19: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: – kv 20: qwen2.rope.freq_base f32 = 1000000.000000
llama_model_loader: – kv 21: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 22: general.file_type u32 = 7
llama_model_loader: – kv 23: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 24: tokenizer.ggml.pre str = qwen2
llama_model_loader: – kv 25: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", …
llama_model_loader: – kv 26: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
llama_model_loader: – kv 27: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",…
llama_model_loader: – kv 28: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: – kv 29: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: – kv 30: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: – kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: – kv 32: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>…
llama_model_loader: – kv 33: general.quantization_version u32 = 2
llama_model_loader: – kv 34: quantize.imatrix.file str = /models_out/Qwen2.5-7B-Instruct-GGUF/…
llama_model_loader: – kv 35: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: – kv 36: quantize.imatrix.entries_count i32 = 196
llama_model_loader: – kv 37: quantize.imatrix.chunks_count i32 = 128
llama_model_loader: – type f32: 141 tensors
llama_model_loader: – type q8_0: 198 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q8_0
print_info: file size = 7.54 GiB (8.50 BPW)
load: printing all EOG tokens:
load: – 151643 ('<|endoftext|>')
load: – 151645 ('<|im_end|>')
load: – 151662 ('<|fim_pad|>')
load: – 151663 ('<|repo_name|>')
load: – 151664 ('<|file_sep|>')
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch = qwen2
print_info: vocab_only = 0
print_info: n_ctx_train = 32768
print_info: n_embd = 3584
print_info: n_layer = 28
print_info: n_head = 28
print_info: n_head_kv = 4
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 7
print_info: n_embd_k_gqa = 512
print_info: n_embd_v_gqa = 512
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 18944
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 1000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 32768
print_info: rope_finetuned = unknown
print_info: model type = 7B
print_info: model params = 7.62 B
print_info: general.name = Qwen2.5 7B Instruct
print_info: vocab type = BPE
print_info: n_vocab = 152064
print_info: n_merges = 151387
print_info: BOS token = 151643 '<|endoftext|>'
print_info: EOS token = 151645 '<|im_end|>'
print_info: EOT token = 151645 '<|im_end|>'
print_info: PAD token = 151643 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: FIM PRE token = 151659 '<|fim_prefix|>'
print_info: FIM SUF token = 151661 '<|fim_suffix|>'
print_info: FIM MID token = 151660 '<|fim_middle|>'
print_info: FIM PAD token = 151662 '<|fim_pad|>'
print_info: FIM REP token = 151663 '<|repo_name|>'
print_info: FIM SEP token = 151664 '<|file_sep|>'
print_info: EOG token = 151643 '<|endoftext|>'
print_info: EOG token = 151645 '<|im_end|>'
print_info: EOG token = 151662 '<|fim_pad|>'
print_info: EOG token = 151663 '<|repo_name|>'
print_info: EOG token = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while… (mmap = true)
load_tensors: offloading 28 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 29/29 layers to GPU
load_tensors: CUDA0 model buffer size = 7165.44 MiB
load_tensors: CPU_Mapped model buffer size = 552.23 MiB
……………………………………………………………………………
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch = 128
llama_context: n_ubatch = 128
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 = 76.00 MiB
llama_context: CUDA_Host compute buffer size = 3.75 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
system_info: n_threads = 8 (n_threads_batch = 8) / 8 | CUDA : ARCHS = 500,610,700,750,800,860,890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
sampler seed: 1862954863
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 = 128, n_predict = 192, n_keep = 0
In <=60 words, explain how you reason and list 5 limitations. Reasoning: I analyze the situation, identify key factors, and consider potential outcomes before making a decision.
Limitations:
1. Limited time for thorough analysis.
2. Uncertainty in predicting future events.
3. Subjectivity in interpreting data.
4. Incomplete information available.
5. Personal biases influencing judgment. Limitations:
1. Time constraints.
2. Uncertainty.
3. Subjectivity.
4. Incomplete info.
5. Bias. Limitations:
1. Time.
2. Uncertainty.
3. Subjectivity.
4. Incomplete info.
5. Bias. Limitations:
1. Time.
2. Uncertainty.
3. Subjectivity.
4. Info gaps.
5. Bias. Limitations:
1. Time.
2. Uncertainty.
3. Subjectivity.
4. Information gaps.
5. Bias. Limitations:
1. Time.
2. Uncertainty.
3. Subjectivity.
4. Incomplete info
llama_perf_sampler_print: sampling time = 23.24 ms / 208 runs ( 0.11 ms per token, 8949.70 tokens per second)
llama_perf_context_print: load time = 1719.25 ms
llama_perf_context_print: prompt eval time = 37.34 ms / 16 tokens ( 2.33 ms per token, 428.53 tokens per second)
llama_perf_context_print: eval time = 4883.79 ms / 191 runs ( 25.57 ms per token, 39.11 tokens per second)
llama_perf_context_print: total time = 4996.36 ms / 207 tokens
llama_perf_context_print: graphs reused = 190
Nice! That’s a clean Q8 run—and we’ve got the numbers:
- Q8 @ 4k, b128 → ~39.1 tokens/sec
- Earlier Q5 @ 4k, b256 → ~53.5 tokens/sec
So Q8 is ~27% slower on your 3060, which matches expectations. VRAM looks fine (model buffer ~7.0–7.2 GiB + KV ~0.22 GiB → ~7.5–7.8 GiB total while idle).
A couple quick tweaks you can try:
If you want a bit more speed at 4k
If VRAM allows, bump batch:
./build/bin/llama-cli \
-m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 4096 -b 256 -t $(nproc) \
-no-cnv -n 192 -p "test"
(Watch nvidia-smi; if it gets tight, drop back to -b 128.)
If you want to try 16k safely
Start conservative and only scale up if stable:
# Step 1: small batch
./build/bin/llama-cli -m models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf \
-ngl 999 -c 16384 -b 64 -t $(nproc) -no-cnv -n 128 -p "test"
# If VRAM is still tight:
./build/bin/llama-cli -m ...Q8_0.gguf -ngl 999 -c 16384 -b 32 --no-kv-offload -t $(nproc) -no-cnv -n 128 -p "test"
# As a last resort (frees more VRAM, slower):
./build/bin/llama-cli -m ...Q8_0.gguf -ngl 64 -c 16384 -b 32 --no-kv-offload -t $(nproc) -no-cnv -n 128 -p "test"
Reduce that repetition you saw
Your sample output repeated “Limitations…” a bunch. Nudge the sampler:
# Slightly stronger anti-repeat
--repeat-penalty 1.1 --repeat-last-n 256
# Or try Mirostat (more stable lengths, sometimes less repeat)
--mirostat 2 --mirostat-lr 0.1 --mirostat-ent 5
Example:
./build/bin/llama-cli -m ...Q8_0.gguf -ngl 999 -c 4096 -b 128 \
--repeat-penalty 1.1 --repeat-last-n 256 \
-no-cnv -n 192 -p "In <=60 words, explain how you reason and list 5 limitations."
Recommendation
- If Q8’s answers feel noticeably cleaner, keep it as your “quality” profile.
- Otherwise, Q5 is the better daily driver (faster, still solid quality), and you can always switch to Q8 for tricky prompts.
Handy aliases (drop in ~/.bashrc)
alias oogway-q5='~/ougway_env/llama.cpp/build/bin/llama-cli -m ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q5_K_M.gguf -ngl 999 -c 4096 -b 256 -t $(nproc)'
alias oogway-q8='~/ougway_env/llama.cpp/build/bin/llama-cli -m ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf -ngl 999 -c 4096 -b 128 -t $(nproc)'
alias oogway-q8-16k='~/ougway_env/llama.cpp/build/bin/llama-cli -m ~/ougway_env/llama.cpp/models/qwen2.5-7b-instruct/Qwen2.5-7B-Instruct-Q8_0.gguf -ngl 999 -c 16384 -b 32 --no-kv-offload -t $(nproc)'
If you want, run one 16k test and tell me peak VRAM + tokens/sec. I’ll lock in final “daily” and “stretch” presets for Little Oogway.
