LLAMA-RETRIEVAL(1)

💖 llama-retrieval(1) — User Commands 💖

okay so bestie, this is the manual page for llama-retrieval, and she lives in section 1, User Commands. this is part 1 of 2, so buckle up. we're keeping every flag, every default, every env var, every alias, every example. the vibe is group chat, but the facts are untouched. periodt. 💅

✨ NAME ✨

llama-retrieval — llama-retrieval. that's the whole name. iconic, honestly.

🦋 DESCRIPTION 🦋

this page is basically a giant list of common params, and there are so many. lowkey a whole buffet of options. don't worry, bestie, we're going through all of them. no skipping, no summarising, no cap. 🫶

💫 ----- common params ----- 💫

• -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) (env: LLAMA_ARG_LOAD_MODE)
  • 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
• --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]
• (okay bestie this is still the same option from part 1, we're just picking it up mid-sentence — it's the Docker Hub one) — 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 it 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)
• -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) 🔥ALL THE MESSAGES
• --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) 😭 it just will NOT 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 lives 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) ⚠️ it's giving experimental, bestie (env: LLAMA_ARG_BACKEND_SAMPLING)

---

🌈 ----- example-specific params ----- 🦋

• --verbose-prompt — print a verbose prompt before generation (default: false)
• --chunks N — max number of chunks to process (default: -1, -1 = all)
• --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)
• --context-file FNAME — file to load context from (use comma-separated values to specify multiple files)
• --chunk-size N — minimum length of embedded text chunks (default: 64)
• --chunk-separator STRING — separator between chunks (default: ' ')

---

🎀 example usage 🎀

like literally just run this, no cap:

bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .

---

and that's the whole page, bestie 🫶 you made it to the end, main character energy fr fr. this one's from the debian folks, dated September 2026, and it's page LLAMA-RETRIEVAL(1) of your local manual — go forth and retrieve 💖✨