LLAMA-DEBUG(1)

💖 LLAMA-DEBUG(1) 💖

(User Commands)

okay bestie, welcome to the llama-debug page! it's giving "I debug therefore I am" energy, and I'm gonna walk you through every single flag like we're texting in the group chat. no cap, nothing is skipped — every option, default, env var, deprecated warning, and example is still here, fr fr. 💅✨

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🌸 NAME 🌸

llama-debug - llama-debug

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✨ DESCRIPTION ✨

💅 ----- common params ----- 💅

okay so these are the common params. there are a LOT, so grab your emotional support beverage 🧋 and let's go. every single one matters, bestie. 🎀

• -h, --help, --usage — print usage and exit 💅
• --version — show version and build info ✨
• -cl, --cache-list — show list of models in cache 🎀
• --completion-bash — print source-able bash completion script for llama.cpp 🐚
• -t, --threads N — number of CPU threads to use during generation (default: <num_cpus>). (env: LLAMA_ARG_THREADS) 🧵
• -tb, --threads-batch N — number of threads to use during batch and prompt processing (default: same as --threads) ⚡
• -C, --cpu-mask M — CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") 🎯
• -Cr, --cpu-range lo-hi — range of CPUs for affinity. Complements --cpu-mask 🎯
• --cpu-strict <0|1> — use strict CPU placement (default: 0) 🔒
• --prio N — set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0) ⚖️
• --poll <0...100> — use polling level to wait for work (0 - no polling) (default: 50) 📊
• -Cb, --cpu-mask-batch M — CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) 🎯
• -Crb, --cpu-range-batch lo-hi — ranges of CPUs for affinity. Complements --cpu-mask-batch 🎯
• --cpu-strict-batch <0|1> — use strict CPU placement (default: same as --cpu-strict) 🔒
• --prio-batch N — set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) ⚖️
• --poll-batch <0|1> — use polling to wait for work (default: same as --poll) 📊
• -c, --ctx-size N — size of the prompt context (default: 0, 0 = loaded from model). (env: LLAMA_ARG_CTX_SIZE) 🧠
• -n, --predict, --n-predict N — number of tokens to predict (default: -1, -1 = infinity). (env: LLAMA_ARG_N_PREDICT) 🔮
• -b, --batch-size N — logical maximum batch size (default: 2048). (env: LLAMA_ARG_BATCH) 📦
• -ub, --ubatch-size N — physical maximum batch size (default: 512). (env: LLAMA_ARG_UBATCH) 📦
• --keep N — number of tokens to keep from the initial prompt (default: 0, -1 = all) 🫶
• --swa-full — use full-size SWA cache (default: false) (more info: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055). (env: LLAMA_ARG_SWA_FULL) 🌊
• -fa, --flash-attn [on|off|auto] — set Flash Attention use ('on', 'off', or 'auto') (default: 'auto'). (env: LLAMA_ARG_FLASH_ATTN) ⚡
• -p, --prompt PROMPT — prompt to start generation with; for system message, use -sys 💬
• --perf, --no-perf — whether to enable internal libllama performance timings (default: false). (env: LLAMA_ARG_PERF) ⏱️
• -f, --file FNAME — a file containing the prompt (default: none) 📄
• -bf, --binary-file FNAME — binary file containing the prompt (default: none) 🧬
• -e, --escape, --no-escape — whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) 🪄
• --rope-scaling {none,linear,yarn} — RoPE frequency scaling method, defaults to linear unless specified by the model. (env: LLAMA_ARG_ROPE_SCALING_TYPE) 🌀
• --rope-scale N — RoPE context scaling factor, expands context by a factor of N. (env: LLAMA_ARG_ROPE_SCALE) 🌀
• --rope-freq-base N — RoPE base frequency, used by NTK-aware scaling (default: loaded from model). (env: LLAMA_ARG_ROPE_FREQ_BASE) 🎛️
• --rope-freq-scale N — RoPE frequency scaling factor, expands context by a factor of 1/N. (env: LLAMA_ARG_ROPE_FREQ_SCALE) 🎛️
• --yarn-orig-ctx N — YaRN: original context size of model (default: 0 = model training context size). (env: LLAMA_ARG_YARN_ORIG_CTX) 🧶
• --yarn-ext-factor N — YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation). (env: LLAMA_ARG_YARN_EXT_FACTOR) 🧶
• --yarn-attn-factor N — YaRN: scale sqrt(t) or attention magnitude (default: -1.00). (env: LLAMA_ARG_YARN_ATTN_FACTOR) 🧶
• --yarn-beta-slow N — YaRN: high correction dim or alpha (default: -1.00). (env: LLAMA_ARG_YARN_BETA_SLOW) 🧶
• --yarn-beta-fast N — YaRN: low correction dim or beta (default: -1.00). (env: LLAMA_ARG_YARN_BETA_FAST) 🧶
• -kvo, --kv-offload, -nkvo, --no-kv-offload — whether to enable KV cache offloading (default: enabled). (env: LLAMA_ARG_KV_OFFLOAD) 💾
• --repack, -nr, --no-repack — whether to enable weight repacking (default: enabled). (env: LLAMA_ARG_REPACK) 📦
• --no-host — bypass host buffer allowing extra buffers to be used. (env: LLAMA_ARG_NO_HOST) 🚫
• -ctk, --cache-type-k TYPE — KV cache data type for K allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16). (env: LLAMA_ARG_CACHE_TYPE_K) 🧮
• -ctv, --cache-type-v TYPE — KV cache data type for V allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16). (env: LLAMA_ARG_CACHE_TYPE_V) 🧮
• -dt, --defrag-thold N — KV cache defragmentation threshold (DEPRECATED). (env: LLAMA_ARG_DEFRAG_THOLD) 💀
• -np, --parallel N — number of parallel sequences to decode (default: 1). (env: LLAMA_ARG_N_PARALLEL) 🧵
• --mlock — DEPRECATED in favor of --load-mode: force system to keep model in RAM rather than swapping or compressing. (env: LLAMA_ARG_MLOCK) 💀
• --mmap, --no-mmap — DEPRECATED in favor of --load-mode: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock). (env: LLAMA_ARG_MMAP) 💀
• -dio, --direct-io, -ndio, --no-direct-io — DEPRECATED in favor of --load-mode: use DirectIO if available. (env: LLAMA_ARG_DIO) 💀
• -lm, --load-mode MODE — model loading mode (default: auto).
  • auto: mmap, unless a device does not support it
  • none: no special loading mode
  • mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
  • mlock: force system to keep model in RAM rather than swapping or compressing
  • mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
  • dio: use DirectIO if available (env: LLAMA_ARG_LOAD_MODE) 🚀
• --numa TYPE — attempt optimizations that help on some NUMA systems
  • distribute: spread execution evenly over all nodes
  • isolate: only spawn threads on CPUs on the node that execution started on
  • numactl: use the CPU map provided by numactl if run without this previously, it is recommended to drop the system page cache before using this see https://github.com/ggml-org/llama.cpp/issues/1437 (env: LLAMA_ARG_NUMA) 🧠
• -dev, --device <dev1,dev2,..> — comma-separated list of devices to use for offloading (none = don't offload) use --list-devices to see a list of available devices. (env: LLAMA_ARG_DEVICE) 🖥️
• --list-devices — print list of available devices and exit 📋
• -ot, --override-tensor <tensor name pattern>=<buffer type>,... — override tensor buffer type. (env: LLAMA_ARG_OVERRIDE_TENSOR) 🔧
• -cmoe, --cpu-moe — keep all Mixture of Experts (MoE) weights in the CPU. (env: LLAMA_ARG_CPU_MOE) 🧠
• -ncmoe, --n-cpu-moe N — keep the Mixture of Experts (MoE) weights of the first N layers in the CPU. (env: LLAMA_ARG_N_CPU_MOE) 🧠
• -ngl, --gpu-layers, --n-gpu-layers N — max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto). (env: LLAMA_ARG_N_GPU_LAYERS) 🎮
• -sm, --split-mode {none,layer,row,tensor} — how to split the model across multiple GPUs, one of:
  • none: use one GPU only
  • layer (default): split layers and KV across GPUs (pipelined)
  • row: split weight across GPUs by rows (parallelized)
  • tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL) (env: LLAMA_ARG_SPLIT_MODE) 🎮
• -ts, --tensor-split N0,N1,N2,... — fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1. (env: LLAMA_ARG_TENSOR_SPLIT) 🎮
• -mg, --main-gpu INDEX — the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0). (env: LLAMA_ARG_MAIN_GPU) 🎮
• -fit, --fit [on|off] — whether to adjust unset arguments to fit in device memory ('on' or 'off') (default: 'on'). (env: LLAMA_ARG_FIT) 📐
• -fitt, --fit-target MiB0,MiB1,MiB2,... — target margin per device for --fit, comma-separated list of values, single value is broadcast across all devices, default: 1024. (env: LLAMA_ARG_FIT_TARGET) 📐
• -fitc, --fit-ctx N — minimum ctx size that can be set by --fit option, default: 4096. (env: LLAMA_ARG_FIT_CTX) 📐
• --check-tensors — check model tensor data for invalid values (default: false) ✅
• --override-kv KEY=TYPE:VALUE,... — advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values. types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false 🔑
• --op-offload, --no-op-offload — whether to offload host tensor operations to device (default: true) 🚀
• --lora FNAME — path to LoRA adapter (use comma-separated values to load multiple adapters) 🎀
• --lora-scaled FNAME:SCALE,... — path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...) note: use comma-separated values 🎀
• --control-vector FNAME — add a control vector note: use comma-separated values to add multiple control vectors 🎛️
• --control-vector-scaled FNAME:SCALE,... — add a control vector with user defined scaling SCALE note: use comma-separated values (format: FNAME:SCALE,...) 🎛️
• --control-vector-layer-range START END — layer range to apply the control vector(s) to, start and end inclusive 🎛️
• -m, --model FNAME — model path to load. (env: LLAMA_ARG_MODEL) 📂
• -mu, --model-url MODEL_URL — model download url (default: unused). (env: LLAMA_ARG_MODEL_URL) 🌐
• -dr, --docker-repo [<repo>/]<model>[:quant]

Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest. example: gemma3 (default: unused) (env: LLAMA_ARG_DOCKER_REPO) 💅

• -hf, -hfr, --hf-repo <user>/<model>[:quant] — the Hugging Face model repository bestie 🦋 quant is optional and case-insensitive, defaults to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist. mmproj also gets downloaded automatically if it's available — to disable that, add --no-mmproj. example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M (default: unused) (env: LLAMA_ARG_HF_REPO)
• -hff, --hf-file FILE — Hugging Face model file. If you specify it, it will override the quant in --hf-repo (default: unused) (env: LLAMA_ARG_HF_FILE) ✨
• -hft, --hf-token TOKEN — Hugging Face access token (default: value from HF_TOKEN environment variable) (env: HF_TOKEN) 🔑
• --log-disable — Log disable. that's it. that's the description. it disables logging, no cap 💀
• --log-file FNAME — Log to file (env: LLAMA_ARG_LOG_FILE) 🍓
• --log-colors [on|off|auto] — Set colored logging ('on', 'off', or 'auto', default: 'auto'). 'auto' enables colors when output is to a terminal (env: LLAMA_ARG_LOG_COLORS) 🌈
• -v, --verbose, --log-verbose — Set verbosity level to infinity (i.e. log all messages, useful for debugging) — infinity!! every message!! 🔥
• --offline — Offline mode: forces use of cache, prevents network access (env: LLAMA_ARG_OFFLINE) 🫶
• -lv, --verbosity, --log-verbosity N — Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:
  • 0: generic output
  • 1: error
  • 2: warning
  • 3: info
  • 4: trace (more info)
  • 5: debug

(default: 3) (env: LLAMA_ARG_LOG_VERBOSITY)

• --log-prefix, --no-log-prefix — Enable prefix in log messages (env: LLAMA_ARG_LOG_PREFIX)
• --log-timestamps, --no-log-timestamps — Enable timestamps in log messages (env: LLAMA_ARG_LOG_TIMESTAMPS) ⏰
• --spec-draft-type-k, -ctkd, --cache-type-k-draft TYPE — KV cache data type for K for the draft model. allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_K)
• --spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE — KV cache data type for V for the draft model. allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default: f16) (env: LLAMA_ARG_SPEC_DRAFT_CACHE_TYPE_V) 💜

---

💫 ----- sampling params ----- 💫

• --samplers SAMPLERS — samplers that will be used for generation, in the order you list them, separated by ; (default: penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature) ✨
• -s, --seed SEED — RNG seed (default: -1, use random seed for -1) 🎲
• --sampler-seq, --sampling-seq SEQUENCE — simplified sequence for samplers that will be used (default: edskypmxt)
• --ignore-eos — ignore end of stream token and continue generating (implies --logit-bias EOS-inf) — she does NOT want to stop 💀
• --temp, --temperature N — temperature (default: 0.80) 🌡️
• --top-k N — top-k sampling (default: 40, 0 = disabled) (env: LLAMA_ARG_TOP_K)
• --top-p N — top-p sampling (default: 0.95, 1.0 = disabled)
• --min-p N — min-p sampling (default: 0.05, 0.0 = disabled)
• --top-nsigma, --top-n-sigma N — top-n-sigma sampling (default: -1.00, -1.0 = disabled)
• --xtc-probability N — xtc probability (default: 0.00, 0.0 = disabled)
• --xtc-threshold N — xtc threshold (default: 0.10, 1.0 = disabled)
• --typical, --typical-p N — locally typical sampling, parameter p (default: 1.00, 1.0 = disabled)
• --repeat-last-n N — last n tokens to consider for penalize (default: 64, 0 = disabled)
• --repeat-penalty N — penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled)
• --presence-penalty N — repeat alpha presence penalty (default: 0.00, 0.0 = disabled)
• --frequency-penalty N — repeat alpha frequency penalty (default: 0.00, 0.0 = disabled)
• --dry-multiplier N — set DRY sampling multiplier (default: 0.00, 0.0 = disabled)
• --dry-base N — set DRY sampling base value (default: 1.75)
• --dry-allowed-length N — set allowed length for DRY sampling (default: 2)
• --dry-penalty-last-n N — set DRY penalty for the last n tokens (default: 64, 0 = disable)
• --dry-sequence-breaker STRING — add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers
• --adaptive-target N — adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00) (more info at https://github.com/ggml-org/llama.cpp/pull/17927) 🦋
• --adaptive-decay N — adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable. (valid range 0.0 to 0.99) (default: 0.90)
• --dynatemp-range N — dynamic temperature range (default: 0.00, 0.0 = disabled)
• --dynatemp-exp N — dynamic temperature exponent (default: 1.00)
• --mirostat N — use Mirostat sampling. Top K, Nucleus and Locally Typical samplers are ignored if used. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) ⭐
• --mirostat-lr N — Mirostat learning rate, parameter eta (default: 0.10)
• --mirostat-ent N — Mirostat target entropy, parameter tau (default: 5.00)
• -l, --logit-bias TOKEN_ID(+/-)BIAS — modifies the likelihood of token appearing in the completion, i.e. --logit-bias 15043+1 to increase likelihood of token ' Hello', or --logit-bias 15043-1 to decrease likelihood of token ' Hello' 💅
• --grammar GRAMMAR — BNF-like grammar to constrain generations (see samples in grammars/ dir)
• --grammar-file FNAME — file to read grammar from
• -j, --json-schema SCHEMA — JSON schema to constrain generations (https://json-schema.org/), e.g. {} for any JSON object. For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
• -jf, --json-schema-file FILE — File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. {} for any JSON object. For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
• -bs, --backend-sampling — enable backend sampling (experimental) (default: disabled) (env: LLAMA_ARG_BACKEND_SAMPLING) — it's experimental bestie, tread carefully 🙈

---

🌸 ----- example-specific params ----- 🌸

• --warmup, --no-warmup — whether to perform warmup with an empty run (default: enabled)
• --pooling {none,mean,cls,last,rank} — pooling type for embeddings, use model default if unspecified (env: LLAMA_ARG_POOLING)
• --embd-normalize N — normalisation for embeddings (default: 2) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
• --embedding, --embeddings — restrict to only support embedding use case; use only with dedicated embedding models (default: disabled) (env: LLAMA_ARG_EMBEDDINGS)
• --save-logits — save final logits to files for verification (default: false)
• --logits-output-dir PATH — directory for saving logits output files (default: data)
• --tensor-filter REGEX — filter tensor names for debug output (regex pattern, can be specified multiple times) 🎀

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👑 example usage:

okay so like, you're gonna want to actually see this in action, so here:

Print tensors:

llama-debug -m model.gguf -p "Hello my name is" --verbose

The tensors to be printed can be filtered with --tensor-filter option.

Save logits/embeddings:

llama-debug -m model.gguf -p "Hello my name is" --save-logits

Add --embedding to save embeddings. 💚

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and that's a wrap bestie 🫶 this page is from the debian package, dated September 2026, and it's serving LLAMA-DEBUG(1) realness — go read a log, you're a main character now 💖✨👑