LLAMA-LOOKUP(1)

✨ NAME ✨

llama-lookup — it's literally just llama-lookup, bestie. That's the name. That's the whole thing. 🫶

🌸 DESCRIPTION 🌸

okay so buckle UP because this is the big one and there are SO many options 💖 These are all the common params — the shared flags that basically every llama.cpp tool understands. We're going through every single one of them together, no skipping, no skimming, keeping every single default and env var because details are the whole vibe. ✨

💜 ----- common params ----- 💜

• -h, --help, --usage — prints the usage text and then peaces out. 👋
• --version — spits out the version + the build info. iconic. ✨
• -cl, --cache-list — shows you the list of models that are chilling in the cache. 🗂️
• --completion-bash — prints a bash completion script for llama.cpp that you can source. source-able, slay. 🐚
• -t, --threads N — how many 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: an arbitrarily long hex string. It complements cpu-range (default: "").
• -Cr, --cpu-range lo-hi — a 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 this 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, where 0 = loaded from the model). (env: LLAMA_ARG_CTX_SIZE)
• -n, --predict, --n-predict N — number of tokens to predict (default: -1, where -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, and -1 means keep 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 — the prompt to start generation with. and listen, for a 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 escape sequences (\n, \r, \t, \', \", \\) (default: true).
• --rope-scaling {none,linear,yarn} — RoPE frequency scaling method. defaults to linear unless the model says otherwise. (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 the model (default: 0 = model training context size). (env: LLAMA_ARG_YARN_ORIG_CTX)
• --yarn-ext-factor N — YaRN: extrapolation mix factor (default: -1.00, where 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 the host buffer, which lets extra buffers 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 — she's on the way out, bestie. (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 the system to keep the model in RAM instead of swapping or compressing it. (env: LLAMA_ARG_MLOCK)
• --mmap, --no-mmap — ⚠️ DEPRECATED in favor of --load-mode: whether to memory-map the model. (if mmap is disabled, load is slower but it may reduce pageouts if you're not using mlock.) (env: LLAMA_ARG_MMAP)
• -dio, --direct-io, -ndio, --no-direct-io — ⚠️ DEPRECATED in favor of --load-mode: use DirectIO if it's available. (env: LLAMA_ARG_DIO)
• -lm, --load-mode MODE — model loading mode (default: auto). the modes are:
  • auto: mmap, unless a device doesn't support it
  • none: no special loading mode
  • mmap: memory-map the model (if mmap is disabled, slower load but may reduce pageouts if you're not using mlock)
  • mlock: force the system to keep the model in RAM rather than swapping or compressing
  • mmap+mlock: mmap plus force the system to keep the 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. the options:
  • 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 you ran without this previously, it's 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,..> — a comma-separated list of devices to use for offloading (none = don't offload). use --list-devices to see the available devices. (env: LLAMA_ARG_DEVICE)
• --list-devices — print the 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 the 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, a 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, a comma-separated list of values; a single value gets broadcast across all devices, default: 1024. (env: LLAMA_ARG_FIT_TARGET)
• -fitc, --fit-ctx N — minimum ctx size that can be set by the --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 the device (default: true).
• --lora FNAME — path to a LoRA adapter (use comma-separated values to load multiple adapters).
• --lora-scaled FNAME:SCALE,... — path to a 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 — yes bestie, this one got its header lopped off, but the details are: repo is optional and defaults to ai/. quant is optional and defaults to :latest. example: gemma3 (default: unused) (env: LLAMA_ARG_DOCKER_REPO)
• -hf, -hfr, --hf-repo <user>/<model>[:quant] — pull from a Hugging Face model repository 🦋. quant is optional, case-insensitive, defaults to Q4_K_M, and it 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 — add --no-mmproj to shut that off. example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M (default: unused) (env: LLAMA_ARG_HF_REPO)
• -hff, --hf-file FILE — a specific Hugging Face model file. if you set this, it overrides the quant in --hf-repo. (default: unused) (env: LLAMA_ARG_HF_FILE)
• -hft, --hf-token TOKEN — your Hugging Face access token 🔑 (default: value from the HF_TOKEN environment variable) (env: HF_TOKEN)
• --log-disable — Log disable. that's the entire description, no notes 💅
• --log-file FNAME — log to a file instead of vibing in your terminal. (env: LLAMA_ARG_LOG_FILE)
• --log-colors [on|off|auto] — colored logging, pick 'on', 'off', or 'auto'. default: 'auto'. 'auto' turns colors on when the output is going to a terminal. (env: LLAMA_ARG_LOG_COLORS)
• -v, --verbose, --log-verbose — cranks the verbosity level to infinity ✨ (i.e. log all messages, which is extremely useful for debugging).
• --offline — offline mode: forces use of cache and prevents network access. no wifi, no problem. (env: LLAMA_ARG_OFFLINE)
• -lv, --verbosity, --log-verbosity N — sets the verbosity threshold. messages with a higher verbosity get 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 — the samplers that will be used for generation, in order, 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, and -1 means use a random seed)
• --sampler-seq, --sampling-seq SEQUENCE — a simplified sequence for the samplers that will be used (default: edskypmxt)
• --ignore-eos — ignore the end of stream token and keep generating (implies --logit-bias EOS-inf)
• --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 a 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 the DRY sampling multiplier (default: 0.00, 0.0 = disabled)
• --dry-base N — set the DRY sampling base value (default: 1.75)
• --dry-allowed-length N — set the allowed length for DRY sampling (default: 2)
• --dry-penalty-last-n N — set the DRY penalty for the last n tokens (default: 64, 0 = disable)
• --dry-sequence-breaker STRING — add a sequence breaker for DRY sampling, which clears out the default breakers ('\n', ':', '"', '*') in the process. use "none" if you want no sequence breakers at all.
• --adaptive-target N — adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00) more info here: 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 a token appearing in the completion, i.e. --logit-bias 15043+1 to increase the likelihood of token ' Hello', or --logit-bias 15043-1 to decrease the likelihood of token ' Hello'
• --grammar GRAMMAR — BNF-like grammar to constrain generations (see samples in grammars/ dir)
• --grammar-file FNAME — file to read the 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)

💀 ----- speculative params ----- 💀

• --spec-draft-n-max N — number of tokens to draft for speculative decoding (default: 3) (env: LLAMA_ARG_SPEC_DRAFT_N_MAX)
• --spec-draft-n-min N — minimum number of draft tokens to use for speculative decoding (default: 0) (env: LLAMA_ARG_SPEC_DRAFT_N_MIN)
• --draft, --draft-n, --draft-max N — ⚠️ the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max (env: LLAMA_ARG_DRAFT_MAX)
• --draft-min, --draft-n-min N — ⚠️ the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min (env: LLAMA_ARG_DRAFT_MIN)

🎀 ----- example-specific params ----- 🎀

• -co, --color [on|off|auto] — colorize output so you can tell prompt and user input apart from the generations ('on', 'off', or 'auto', default: 'auto'). 'auto' turns colors on when the output is going to a terminal.
• -lcs, --lookup-cache-static FNAME — path to the static lookup cache to use for lookup decoding (not updated by generation)
• -lcd, --lookup-cache-dynamic FNAME — path to the dynamic lookup cache to use for lookup decoding (updated by generation)

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and that's the whole menu, bestie 🫶 this page ships on debian, dated September 2026, and it's page one of LLAMA-LOOKUP(1) — go forth and look things up, you ate 💅✨