$ ./llama-cli --help
load_backend: loaded RPC backend from /home/minimonk/src/llama-b8876/libggml-rpc.so
load_backend: loaded CPU backend from /home/minimonk/src/llama-b8876/libggml-cpu-haswell.so
----- common params -----
-h, --help, --usage print usage and exit
--version show version and build info
--license show source code license and dependencies
-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: -1)
(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)
--rpc SERVERS comma separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC)
--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)
-hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]
Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_HFD_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_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_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_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: debug
(default: 1)
(env: LLAMA_LOG_VERBOSITY)
--log-prefix Enable prefix in log messages
(env: LLAMA_LOG_PREFIX)
--log-timestamps Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS)
-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_CACHE_TYPE_K_DRAFT)
-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_CACHE_TYPE_V_DRAFT)
----- 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 -----
--verbose-prompt print a verbose prompt before generation (default: false)
--display-prompt, --no-display-prompt whether to print prompt at generation (default: true)
-co, --color [on|off|auto] Colorize output to distinguish prompt and user input from generations
('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal
-ctxcp, --ctx-checkpoints, --swa-checkpoints N
max number of context checkpoints to create per slot (default:
32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS)
-cpent, --checkpoint-every-n-tokens N create a checkpoint every n tokens during prefill (processing), -1 to
disable (default: 8192)
(env: LLAMA_ARG_CHECKPOINT_EVERY_NT)
-cram, --cache-ram N set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 -
disable)[(more
info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM)
--context-shift, --no-context-shift whether to use context shift on infinite text generation (default:
disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT)
-sys, --system-prompt PROMPT system prompt to use with model (if applicable, depending on chat
template)
--show-timings, --no-show-timings whether to show timing information after each response (default: true)
(env: LLAMA_ARG_SHOW_TIMINGS)
-sysf, --system-prompt-file FNAME a file containing the system prompt (default: none)
-r, --reverse-prompt PROMPT halt generation at PROMPT, return control in interactive mode
-sp, --special special tokens output enabled (default: false)
-cnv, --conversation, -no-cnv, --no-conversation
whether to run in conversation mode:
- does not print special tokens and suffix/prefix
- interactive mode is also enabled
(default: auto enabled if chat template is available)
-st, --single-turn run conversation for a single turn only, then exit when done
will not be interactive if first turn is predefined with --prompt
(default: false)
-mli, --multiline-input allows you to write or paste multiple lines without ending each in '\'
--warmup, --no-warmup whether to perform warmup with an empty run (default: enabled)
-mm, --mmproj FILE path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ)
-mmu, --mmproj-url URL URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL)
--mmproj-auto, --no-mmproj, --no-mmproj-auto
whether to use multimodal projector file (if available), useful when
using -hf (default: enabled)
(env: LLAMA_ARG_MMPROJ_AUTO)
--mmproj-offload, --no-mmproj-offload whether to enable GPU offloading for multimodal projector (default:
enabled)
(env: LLAMA_ARG_MMPROJ_OFFLOAD)
--image, --audio FILE path to an image or audio file. use with multimodal models, use
comma-separated values for multiple files
--image-min-tokens N minimum number of tokens each image can take, only used by vision
models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS)
--image-max-tokens N maximum number of tokens each image can take, only used by vision
models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS)
-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...
override tensor buffer type for draft model
-cmoed, --cpu-moe-draft keep all Mixture of Experts (MoE) weights in the CPU for the draft
model
(env: LLAMA_ARG_CPU_MOE_DRAFT)
-ncmoed, --n-cpu-moe-draft N keep the Mixture of Experts (MoE) weights of the first N layers in the
CPU for the draft model
(env: LLAMA_ARG_N_CPU_MOE_DRAFT)
--chat-template-kwargs STRING sets additional params for the json template parser, must be a valid
json object string, e.g. '{"key1":"value1","key2":"value2"}'
(env: LLAMA_CHAT_TEMPLATE_KWARGS)
--jinja, --no-jinja whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA)
--reasoning-format FORMAT controls whether thought tags are allowed and/or extracted from the
response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `<think>` tags in `message.content` while
also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK)
-rea, --reasoning [on|off|auto] Use reasoning/thinking in the chat ('on', 'off', or 'auto', default:
'auto' (detect from template))
(env: LLAMA_ARG_REASONING)
--reasoning-budget N token budget for thinking: -1 for unrestricted, 0 for immediate end,
N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET)
--reasoning-budget-message MESSAGE message injected before the end-of-thinking tag when reasoning budget
is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE)
--chat-template JINJA_TEMPLATE set custom jinja chat template (default: template taken from model's
metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set
before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml,
command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe,
exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite,
granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2,
llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4,
megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken,
mistral-v7, mistral-v7-tekken, monarch, openchat, orion,
pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open,
vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE)
--chat-template-file JINJA_TEMPLATE_FILE
set custom jinja chat template file (default: template taken from
model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set
before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml,
command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe,
exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite,
granite-4.0, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-ocr, kimi-k2,
llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4,
megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken,
mistral-v7, mistral-v7-tekken, monarch, openchat, orion,
pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open,
vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE)
--skip-chat-parsing, --no-skip-chat-parsing
force a pure content parser, even if a Jinja template is specified;
model will output everything in the content section, including any
reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING)
--simple-io use basic IO for better compatibility in subprocesses and limited
consoles
--draft, --draft-n, --draft-max N number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX)
--draft-min, --draft-n-min N minimum number of draft tokens to use for speculative decoding
(default: 0)
(env: LLAMA_ARG_DRAFT_MIN)
--draft-p-min P minimum speculative decoding probability (greedy) (default: 0.75)
(env: LLAMA_ARG_DRAFT_P_MIN)
-cd, --ctx-size-draft N size of the prompt context for the draft model (default: 0, 0 = loaded
from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT)
-devd, --device-draft <dev1,dev2,..> comma-separated list of devices to use for offloading the draft model
(none = don't offload)
use --list-devices to see a list of available devices
-ngld, --gpu-layers-draft, --n-gpu-layers-draft N
max. number of draft model layers to store in VRAM, either an exact
number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT)
-md, --model-draft FNAME draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT)
--spec-replace TARGET DRAFT translate the string in TARGET into DRAFT if the draft model and main
model are not compatible
--gpt-oss-20b-default use gpt-oss-20b (note: can download weights from the internet)
--gpt-oss-120b-default use gpt-oss-120b (note: can download weights from the internet)
--vision-gemma-4b-default use Gemma 3 4B QAT (note: can download weights from the internet)
--vision-gemma-12b-default use Gemma 3 12B QAT (note: can download weights from the internet)
--spec-default enable default speculative decoding config |
