-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: 128, -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

force system to keep model in RAM rather than swapping or compressing (env: LLAMA_ARG_MLOCK)

--mmap, --no-mmap

whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled) (env: LLAMA_ARG_MMAP)

-dio, --direct-io, -ndio, --no-direct-io

use DirectIO if available. (default: disabled)

(env: LLAMA_ARG_DIO)

--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]

Hugging Face model repository; quant is optional, case-insensitive,

default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist. mmproj is also downloaded automatically if available. to disable, 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 specified, it will override the quant in

--hf-repo (default: unused)

(env: LLAMA_ARG_HF_FILE)

-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]

Hugging Face model repository for the vocoder model (default: unused)

(env: LLAMA_ARG_HF_REPO_V)

-hffv, --hf-file-v FILE Hugging Face model file for the vocoder model (default: unused)

(env: LLAMA_ARG_HF_FILE_V)

-hft, --hf-token TOKEN Hugging Face access token (default: value from HF_TOKEN environment

variable)

(env: HF_TOKEN)

--log-disable

Log disable

--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)

--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, 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)

--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, -1 = ctx_size)

--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: -1, 0 = disable, -1 = context size)

--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)](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)

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

-kvu, --kv-unified, -no-kvu, --no-kv-unified

use single unified KV buffer shared across all sequences (default:

enabled if number of slots is auto) (env: LLAMA_ARG_KV_UNIFIED)

-ns, --sequences N number of sequences to decode (default: 1)

--junk N

number of times to repeat the junk text (default: 1)

-pps is the prompt shared across parallel sequences (default: false) -tgs is the text generation separated across the different sequences

(default: false)