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containers/llamacpp: switch to router mode with presets and add watchdog

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### Lllama.Cpp
----- 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
--verbose-prompt print a verbose prompt before generation (default: false)
-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)
--perf, --no-perf whether to enable internal libllama performance timings (default:
false)
(env: LLAMA_ARG_PERF)
-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)
--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} how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(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: unsloth/phi-4-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: 3)
(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 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 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 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) (default: '')
--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 -----
-lcs, --lookup-cache-static FNAME path to static lookup cache to use for lookup decoding (not updated by
generation)
-lcd, --lookup-cache-dynamic FNAME path to dynamic lookup cache to use for lookup decoding (updated by
generation)
--ctx-checkpoints, --swa-checkpoints N
max number of context checkpoints to create per slot (default:
8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS)
-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)
-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)
--context-shift, --no-context-shift whether to use context shift on infinite text generation (default:
disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT)
-r, --reverse-prompt PROMPT halt generation at PROMPT, return control in interactive mode
-sp, --special special tokens output enabled (default: false)
--warmup, --no-warmup whether to perform warmup with an empty run (default: enabled)
--spm-infill use Suffix/Prefix/Middle pattern for infill (instead of
Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
--pooling {none,mean,cls,last,rank} pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING)
-np, --parallel N number of server slots (default: -1, -1 = auto)
(env: LLAMA_ARG_N_PARALLEL)
-cb, --cont-batching, -nocb, --no-cont-batching
whether to enable continuous batching (a.k.a dynamic batching)
(default: enabled)
(env: LLAMA_ARG_CONT_BATCHING)
-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-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)
-a, --alias STRING set alias for model name (to be used by REST API)
(env: LLAMA_ARG_ALIAS)
--host HOST ip address to listen, or bind to an UNIX socket if the address ends
with .sock (default: 127.0.0.1)
(env: LLAMA_ARG_HOST)
--port PORT port to listen (default: 8080)
(env: LLAMA_ARG_PORT)
--path PATH path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH)
--api-prefix PREFIX prefix path the server serves from, without the trailing slash
(default: )
(env: LLAMA_ARG_API_PREFIX)
--webui-config JSON JSON that provides default WebUI settings (overrides WebUI defaults)
(env: LLAMA_ARG_WEBUI_CONFIG)
--webui-config-file PATH JSON file that provides default WebUI settings (overrides WebUI
defaults)
(env: LLAMA_ARG_WEBUI_CONFIG_FILE)
--webui, --no-webui whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_WEBUI)
--embedding, --embeddings restrict to only support embedding use case; use only with dedicated
embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS)
--rerank, --reranking enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING)
--api-key KEY API key to use for authentication, multiple keys can be provided as a
comma-separated list (default: none)
(env: LLAMA_API_KEY)
--api-key-file FNAME path to file containing API keys (default: none)
--ssl-key-file FNAME path to file a PEM-encoded SSL private key
(env: LLAMA_ARG_SSL_KEY_FILE)
--ssl-cert-file FNAME path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE)
--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)
-to, --timeout N server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT)
--threads-http N number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP)
--cache-prompt, --no-cache-prompt whether to enable prompt caching (default: enabled)
(env: LLAMA_ARG_CACHE_PROMPT)
--cache-reuse N min chunk size to attempt reusing from the cache via KV shifting,
requires prompt caching to be enabled (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE)
--metrics enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS)
--props enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS)
--slots, --no-slots expose slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS)
--slot-save-path PATH path to save slot kv cache (default: disabled)
--media-path PATH directory for loading local media files; files can be accessed via
file:// URLs using relative paths (default: disabled)
--models-dir PATH directory containing models for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_DIR)
--models-preset PATH path to INI file containing model presets for the router server
(default: disabled)
(env: LLAMA_ARG_MODELS_PRESET)
--models-max N for router server, maximum number of models to load simultaneously
(default: 4, 0 = unlimited)
(env: LLAMA_ARG_MODELS_MAX)
--models-autoload, --no-models-autoload
for router server, whether to automatically load models (default:
enabled)
(env: LLAMA_ARG_MODELS_AUTOLOAD)
--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)
--reasoning-budget N controls the amount of thinking allowed; currently only one of: -1 for
unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET)
--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, deepseek2, deepseek3, exaone-moe, exaone3,
exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2,
hunyuan-dense, hunyuan-moe, 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, deepseek2, deepseek3, exaone-moe, exaone3,
exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2,
hunyuan-dense, hunyuan-moe, 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)
--prefill-assistant, --no-prefill-assistant
whether to prefill the assistant's response if the last message is an
assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message
then it will be treated as a full message and not prefilled
(env: LLAMA_ARG_PREFILL_ASSISTANT)
-sps, --slot-prompt-similarity SIMILARITY
how much the prompt of a request must match the prompt of a slot in
order to use that slot (default: 0.10, 0.0 = disabled)
--lora-init-without-apply load LoRA adapters without applying them (apply later via POST
/lora-adapters) (default: disabled)
--sleep-idle-seconds SECONDS number of seconds of idleness after which the server will sleep
(default: -1; -1 = disabled)
-td, --threads-draft N number of threads to use during generation (default: same as
--threads)
-tbd, --threads-batch-draft N number of threads to use during batch and prompt processing (default:
same as --threads-draft)
--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
--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
type of speculative decoding to use when no draft model is provided
(default: none)
--spec-ngram-size-n N ngram size N for ngram-simple/ngram-map speculative decoding, length
of lookup n-gram (default: 12)
--spec-ngram-size-m N ngram size M for ngram-simple/ngram-map speculative decoding, length
of draft m-gram (default: 48)
--spec-ngram-min-hits N minimum hits for ngram-map speculative decoding (default: 1)
-mv, --model-vocoder FNAME vocoder model for audio generation (default: unused)
--tts-use-guide-tokens Use guide tokens to improve TTS word recall
--embd-gemma-default use default EmbeddingGemma model (note: can download weights from the
internet)
--fim-qwen-1.5b-default use default Qwen 2.5 Coder 1.5B (note: can download weights from the
internet)
--fim-qwen-3b-default use default Qwen 2.5 Coder 3B (note: can download weights from the
internet)
--fim-qwen-7b-default use default Qwen 2.5 Coder 7B (note: can download weights from the
internet)
--fim-qwen-7b-spec use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can
download weights from the internet)
--fim-qwen-14b-spec use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note:
can download weights from the internet)
--fim-qwen-30b-default use default Qwen 3 Coder 30B A3B Instruct (note: can download weights
from the internet)
--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)
containers/llamacpp/llamacpp-watchdog.service
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[Unit]
Description=Watchdog llamacpp: riavvia il servizio se /slots smette di rispondere (livelock)
After=llamacpp.service
[Service]
Type=oneshot
ExecStart=%h/scripts/llamacpp-watchdog/llamacpp-watchdog.sh
containers/llamacpp/llamacpp-watchdog.sh
Executable
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#!/bin/bash
# Watchdog per il container llamacpp (rocm): /health risponde "ok" anche
# quando il server e' livelocked - bug osservato il 2026-08-07, dove
# cancellare una richiesta a meta' prompt-processing su uno slot "freddo"
# (nessuna cache riusabile) con speculative decoding attivo lascia il thread
# principale a girare in loop su CPU senza mai liberare lo slot: GPU a 0%,
# nessuna nuova richiesta viene piu' assegnata, ma /health continua a dire
# "ok" perche' non tocca lo stato interno degli slot.
#
# L'unico segnale affidabile trovato e' /slots: si blocca perche' per
# leggere lo stato di ogni slot condivide (probabilmente) un lock col loop
# principale, che nel livelock non lo rilascia mai. Da qui il check.
#
# Uso: pensato per girare come systemd --user timer (vedi
# llamacpp-watchdog.service/.timer), non a mano.
set -euo pipefail
CONTAINER="llamacpp"
SERVICE="llamacpp.service"
CURL_TIMEOUT=5
FAIL_THRESHOLD=3 # controlli /slots falliti consecutivi prima di riavviare
MAX_RESTARTS_PER_WINDOW=3 # oltre questa soglia nella finestra sotto, ci si ferma
RESTART_WINDOW_SEC=3600
STATE_DIR="${XDG_RUNTIME_DIR:-/tmp}/llamacpp-watchdog"
mkdir -p "$STATE_DIR"
FAIL_COUNT_FILE="$STATE_DIR/fail_count"
RESTART_LOG="$STATE_DIR/restart_log"
log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }
# Porta rilevata a runtime invece che hardcoded: il file quadlet nel repo
# puo' disallinearsi da quella realmente pubblicata (successo gia' una volta).
port="$(podman port "$CONTAINER" 2>/dev/null | head -n1 | sed -E 's#.*:([0-9]+)$#\1#')"
if [ -z "$port" ]; then
log "ERRORE: impossibile determinare la porta pubblicata di $CONTAINER (container fermo?), salto il check"
exit 0
fi
fail_count=0
[ -f "$FAIL_COUNT_FILE" ] && fail_count="$(cat "$FAIL_COUNT_FILE")"
if curl -sf -m "$CURL_TIMEOUT" "http://127.0.0.1:${port}/slots" -o /dev/null; then
if [ "$fail_count" -gt 0 ]; then
log "OK: /slots torna a rispondere dopo $fail_count fallimenti, reset contatore"
fi
echo 0 > "$FAIL_COUNT_FILE"
exit 0
fi
fail_count=$((fail_count + 1))
echo "$fail_count" > "$FAIL_COUNT_FILE"
log "ATTENZIONE: /slots non risponde (fallimento $fail_count/$FAIL_THRESHOLD)"
if [ "$fail_count" -lt "$FAIL_THRESHOLD" ]; then
exit 0
fi
# Soglia raggiunta. Prima di riavviare, controlla di non aver gia' riavviato
# troppe volte di recente: se il livelock si ripresenta subito dopo ogni
# restart c'e' un bug persistente da guardare a mano, non da nascondere
# dietro un restart-loop infinito.
now="$(date +%s)"
touch "$RESTART_LOG"
recent_restarts="$(awk -v now="$now" -v window="$RESTART_WINDOW_SEC" '$1 > now - window' "$RESTART_LOG" | wc -l)"
if [ "$recent_restarts" -ge "$MAX_RESTARTS_PER_WINDOW" ]; then
log "CRITICO: $recent_restarts restart gia' effettuati nell'ultima ora, mi fermo qui (serve intervento manuale: il problema si ripresenta troppo in fretta)"
exit 1
fi
log "RIAVVIO: /slots bloccato per $fail_count controlli consecutivi, riavvio $SERVICE"
echo "$now" >> "$RESTART_LOG"
echo 0 > "$FAIL_COUNT_FILE"
systemctl --user restart "$SERVICE"
containers/llamacpp/llamacpp-watchdog.timer
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[Unit]
Description=Timer watchdog llamacpp (ogni minuto)
[Timer]
OnBootSec=2m
OnUnitActiveSec=60s
Persistent=false
[Install]
WantedBy=timers.target
containers/llamacpp/llamacpp.README
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### Lllama.Cpp
# Llama.Cpp — Router Mode (preset) & Watchdog
----- common params -----
Path di riferimento per i flag CLI di `llama-server`: vedi `llamacpp-serve.README`.
-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
--verbose-prompt print a verbose prompt before generation (default: false)
-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)
--perf, --no-perf whether to enable internal libllama performance timings (default:
false)
(env: LLAMA_ARG_PERF)
-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)
--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} how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(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: unsloth/phi-4-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: 3)
(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)
## Indice
- [Router mode & preset (piu' modelli)](#router-mode--preset-piu-modelli)
- [Aggiungere un modello](#aggiungere-un-modello)
- [Watchdog (livelock su /slots)](#watchdog-livelock-su-slots)
----- 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 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 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 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) (default: '')
--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)
## Router mode & preset (piu' modelli)
Il container `llamacpp.container` gira in **router mode**: `llama-server` non
carica un modello fisso ma fa da router, caricando/scaricando i modelli al
volo e selezionandoli per richiesta tramite il campo `"model"` dell'API.
----- example-specific params -----
I preset sono definiti nel file **`/config/preset.ini`** (montato da host in
`/srv/containers/llamacpp/config/preset.ini`), impostato con l'env var
`LLAMA_ARG_MODELS_PRESET`.
-lcs, --lookup-cache-static FNAME path to static lookup cache to use for lookup decoding (not updated by
generation)
-lcd, --lookup-cache-dynamic FNAME path to dynamic lookup cache to use for lookup decoding (updated by
generation)
--ctx-checkpoints, --swa-checkpoints N
max number of context checkpoints to create per slot (default:
8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS)
-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)
-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)
--context-shift, --no-context-shift whether to use context shift on infinite text generation (default:
disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT)
-r, --reverse-prompt PROMPT halt generation at PROMPT, return control in interactive mode
-sp, --special special tokens output enabled (default: false)
--warmup, --no-warmup whether to perform warmup with an empty run (default: enabled)
--spm-infill use Suffix/Prefix/Middle pattern for infill (instead of
Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
--pooling {none,mean,cls,last,rank} pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING)
-np, --parallel N number of server slots (default: -1, -1 = auto)
(env: LLAMA_ARG_N_PARALLEL)
-cb, --cont-batching, -nocb, --no-cont-batching
whether to enable continuous batching (a.k.a dynamic batching)
(default: enabled)
(env: LLAMA_ARG_CONT_BATCHING)
-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-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)
-a, --alias STRING set alias for model name (to be used by REST API)
(env: LLAMA_ARG_ALIAS)
--host HOST ip address to listen, or bind to an UNIX socket if the address ends
with .sock (default: 127.0.0.1)
(env: LLAMA_ARG_HOST)
--port PORT port to listen (default: 8080)
(env: LLAMA_ARG_PORT)
--path PATH path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH)
--api-prefix PREFIX prefix path the server serves from, without the trailing slash
(default: )
(env: LLAMA_ARG_API_PREFIX)
--webui-config JSON JSON that provides default WebUI settings (overrides WebUI defaults)
(env: LLAMA_ARG_WEBUI_CONFIG)
--webui-config-file PATH JSON file that provides default WebUI settings (overrides WebUI
defaults)
(env: LLAMA_ARG_WEBUI_CONFIG_FILE)
--webui, --no-webui whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_WEBUI)
--embedding, --embeddings restrict to only support embedding use case; use only with dedicated
embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS)
--rerank, --reranking enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING)
--api-key KEY API key to use for authentication, multiple keys can be provided as a
comma-separated list (default: none)
(env: LLAMA_API_KEY)
--api-key-file FNAME path to file containing API keys (default: none)
--ssl-key-file FNAME path to file a PEM-encoded SSL private key
(env: LLAMA_ARG_SSL_KEY_FILE)
--ssl-cert-file FNAME path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE)
--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)
-to, --timeout N server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT)
--threads-http N number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP)
--cache-prompt, --no-cache-prompt whether to enable prompt caching (default: enabled)
(env: LLAMA_ARG_CACHE_PROMPT)
--cache-reuse N min chunk size to attempt reusing from the cache via KV shifting,
requires prompt caching to be enabled (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE)
--metrics enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS)
--props enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS)
--slots, --no-slots expose slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS)
--slot-save-path PATH path to save slot kv cache (default: disabled)
--media-path PATH directory for loading local media files; files can be accessed via
file:// URLs using relative paths (default: disabled)
--models-dir PATH directory containing models for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_DIR)
--models-preset PATH path to INI file containing model presets for the router server
(default: disabled)
(env: LLAMA_ARG_MODELS_PRESET)
--models-max N for router server, maximum number of models to load simultaneously
(default: 4, 0 = unlimited)
(env: LLAMA_ARG_MODELS_MAX)
--models-autoload, --no-models-autoload
for router server, whether to automatically load models (default:
enabled)
(env: LLAMA_ARG_MODELS_AUTOLOAD)
--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)
--reasoning-budget N controls the amount of thinking allowed; currently only one of: -1 for
unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET)
--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, deepseek2, deepseek3, exaone-moe, exaone3,
exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2,
hunyuan-dense, hunyuan-moe, 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, deepseek2, deepseek3, exaone-moe, exaone3,
exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2,
hunyuan-dense, hunyuan-moe, 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)
--prefill-assistant, --no-prefill-assistant
whether to prefill the assistant's response if the last message is an
assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message
then it will be treated as a full message and not prefilled
(env: LLAMA_ARG_PREFILL_ASSISTANT)
-sps, --slot-prompt-similarity SIMILARITY
how much the prompt of a request must match the prompt of a slot in
order to use that slot (default: 0.10, 0.0 = disabled)
--lora-init-without-apply load LoRA adapters without applying them (apply later via POST
/lora-adapters) (default: disabled)
--sleep-idle-seconds SECONDS number of seconds of idleness after which the server will sleep
(default: -1; -1 = disabled)
-td, --threads-draft N number of threads to use during generation (default: same as
--threads)
-tbd, --threads-batch-draft N number of threads to use during batch and prompt processing (default:
same as --threads-draft)
--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
--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
type of speculative decoding to use when no draft model is provided
(default: none)
--spec-ngram-size-n N ngram size N for ngram-simple/ngram-map speculative decoding, length
of lookup n-gram (default: 12)
--spec-ngram-size-m N ngram size M for ngram-simple/ngram-map speculative decoding, length
of draft m-gram (default: 48)
--spec-ngram-min-hits N minimum hits for ngram-map speculative decoding (default: 1)
-mv, --model-vocoder FNAME vocoder model for audio generation (default: unused)
--tts-use-guide-tokens Use guide tokens to improve TTS word recall
--embd-gemma-default use default EmbeddingGemma model (note: can download weights from the
internet)
--fim-qwen-1.5b-default use default Qwen 2.5 Coder 1.5B (note: can download weights from the
internet)
--fim-qwen-3b-default use default Qwen 2.5 Coder 3B (note: can download weights from the
internet)
--fim-qwen-7b-default use default Qwen 2.5 Coder 7B (note: can download weights from the
internet)
--fim-qwen-7b-spec use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can
download weights from the internet)
--fim-qwen-14b-spec use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note:
can download weights from the internet)
--fim-qwen-30b-default use default Qwen 3 Coder 30B A3B Instruct (note: can download weights
from the internet)
--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)
Struttura del `preset.ini`:
```ini
version = 1
[*]
; tuning globale, condiviso da tutti i modelli
batch-size = 4096
ubatch-size = 1024
cache-type-k = q4_1
cache-type-v = q4_1
cache-reuse = 256
[qwen36-mtp]
hf = unsloth/Qwen3.6-35B-A3B-MTP-GGUF:UD-IQ4_NL
spec-type = draft-mtp
spec-draft-n-max = 2
[gemma4]
hf = unsloth/gemma-4-26B-A4B-it-GGUF:UD-IQ4_NL
[deepseek]
hf = unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-IQ3_XXS
hf-repo-draft = unsloth/DeepSeek-V4-Flash-0731-GGUF:Q8_0
spec-type = draft-dspark
spec-draft-n-max = 3
n-gpu-layers-draft = 99
```
- Ogni sezione `[nome]` e' un preset/modello; il **nome della sezione** e' il
valore da passare nel campo `"model"` dell'API.
- La chiave `hf` punta a un modello Hugging Face (repo:quant); usa il quant
**esatto** del repo (prefisso `UD-` incluso). In alternativa `model` punta
a un file `.gguf` locale gia' scaricato.
- La sezione `[*]` contiene il tuning globale condiviso (batch, ubatch,
cache-type, cache-reuse).
- I parametri **per-modello** (tipo MTP, ctx dedicato) vanno nella sezione
del modello, NON in `[*]`. `spec-type=draft-mtp` e' valido solo sui modelli
con MTP nativo (qwen36-mtp). DeepSeek usa **DSpark**
(`spec-type=draft-dspark` + `hf-repo-draft` per il draft module separato,
che si scarica da solo da HF), mentre gemma usa un file draft separato da
configurare esplicitamente.
- `LLAMA_ARG_CTX_SIZE` resta come `Environment=` nel `.container` ed e'
ereditato da ogni istanza di modello.
### Uso
```bash
# Elenca i modelli disponibili nel router
curl -s localhost:8090/v1/models
# Chat selezionando il modello tramite il campo "model"
curl -s localhost:8090/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"qwen36-mtp","messages":[{"role":"user","content":"ciao"}]}'
```
### Aggiungere un modello
1. Aggiungi una sezione a `preset.ini` — sia in git
(`containers/llamacpp/preset.ini`) sia in
`/srv/containers/aitools/config/preset.ini` (la copia montata).
Esempio con override per-modello:
```ini
[chat]
hf = unsloth/altro-modello-GGUF:Q4_K_M
ctx-size = 65536
```
2. Riavvia il servizio:
```bash
systemctl --user daemon-reload
systemctl --user restart llamacpp.service
```
> **Nota**: il Model ID cambiato. Prima era il nome del file `.gguf`; ora e'
> il nome della sezione nel preset (es. `qwen36-mtp`). Aggiorna il campo
> "model" nelle integrazioni (es. Odoo > AI).
---
## Watchdog (livelock su /slots)
`llama-server` puo' restare "livelocked" dopo la cancellazione di una
richiesta a meta' prompt-processing su uno slot senza cache riusabile,
specialmente con speculative decoding attivo su contesti molto lunghi: il
thread principale gira in loop su CPU senza mai liberare lo slot, la GPU
resta a 0% e nessuna nuova richiesta viene piu' processata. `/health`
continua pero' a rispondere "ok", quindi non e' un check affidabile;
`/slots` invece si blocca (probabilmente condivide un lock col loop
principale) ed e' il segnale usato dal watchdog qui incluso.
I file sono in `containers/llamacpp/`:
`llamacpp-watchdog.sh`, `llamacpp-watchdog.service`, `llamacpp-watchdog.timer`.
Installazione su host (utente non-root, stesso utente che gestisce i
quadlet):
```bash
mkdir -p ~/scripts/llamacpp-watchdog
cp containers/llamacpp/llamacpp-watchdog.sh ~/scripts/llamacpp-watchdog/
chmod +x ~/scripts/llamacpp-watchdog/llamacpp-watchdog.sh
cp containers/llamacpp/llamacpp-watchdog.service ~/.config/systemd/user/
cp containers/llamacpp/llamacpp-watchdog.timer ~/.config/systemd/user/
systemctl --user daemon-reload
systemctl --user enable --now llamacpp-watchdog.timer
```
Verifica:
```bash
systemctl --user status llamacpp-watchdog.timer
journalctl --user -u llamacpp-watchdog.service -f
```
Il watchdog riavvia `llamacpp.service` dopo 3 controlli `/slots` falliti di
fila (~3 minuti), e si ferma da solo (senza piu' riavviare) se in un'ora
scatta piu' di 3 volte, per non nascondere un bug persistente dietro un
restart-loop infinito - in quel caso va guardato a mano.
containers/llamacpp/llamacpp.caddy
+1 -1
View File
@@ -21,7 +21,7 @@
@embeddings path /v1/embeddings*
reverse_proxy @embeddings [ip_address]:8091
# Chat / completions: container principale (porta 8090, modello Qwen3-Coder)
# Chat / completions: container principale (porta 8090)
reverse_proxy [ip_address]:8090
log {
containers/llamacpp/llamacpp.container
+5 -17
View File
@@ -11,6 +11,7 @@ PublishPort=8090:8090
# Volumes
Volume=/srv/containers/aitools/models/hf:/root/.cache/huggingface/hub
Volume=/srv/containers/aitools/config:/config
# ROCm tuning
AddDevice=/dev/dri/renderD128
@@ -19,21 +20,8 @@ SecurityLabelType=container_runtime_t
Environment=LLAMA_ARG_HOST=0.0.0.0
Environment=LLAMA_ARG_PORT=8090
Environment=LLAMA_ARG_LOAD_MODE=none
Environment=LLAMA_ARG_MODELS_PRESET=/config/preset.ini
Environment=LLAMA_ARG_CTX_SIZE=131072
Environment=LLAMA_ARG_HF_REPO=unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Improvements
Environment=LLAMA_ARG_CACHE_REUSE=256
Environment=LLAMA_ARG_BATCH=4096
Environment=LLAMA_ARG_UBATCH=1024
Environment=LLAMA_ARG_CACHE_TYPE_K=q4_1
Environment=LLAMA_ARG_CACHE_TYPE_V=q4_1
#Environment=LLAMA_CHAT_TEMPLATE_KWARGS={"thinking":true,"reasoning_effort":"medium"}
# MTP
Environment=LLAMA_ARG_SPEC_TYPE=draft-mtp
Environment=LLAMA_ARG_SPEC_DRAFT_N_MAX=6
# RPC — worker ggml-rpc-server (llamacpp-rpc.container), lista host:port; backend insicuro, solo su internal.network
#Environment=LLAMA_ARG_RPC=llamacpp-rpc:50052
@@ -48,9 +36,9 @@ Environment=HF_TOKEN=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
# Decommentare per abilitare l'autenticazione
#Environment=LLAMA_API_KEY=la-tua-chiave-segreta
# Model alias — nome con cui il server risponde alle richieste (campo "model" nell'API)
# Se non impostato, llama-server usa il nome del file .gguf come alias
#Environment=LLAMA_ARG_ALIAS=chat
# Model ID — in router mode il campo "model" dell'API e' il nome della sezione
# del preset (/config/preset.ini), es. "qwen36-mtp". Aggiorna di conseguenza
# il campo "model" nelle integrazioni (es. Odoo > AI > API Key).
# Override Boot
#Entrypoint=/usr/bin/sleep
containers/llamacpp/llamacpp.nginx
+1 -1
View File
@@ -58,7 +58,7 @@ server {
proxy_pass http://[ip_address]:8091;
}
# Chat / completions: container principale (porta 8090, modello Qwen3-Coder)
# Chat / completions: container principale (porta 8090)
location / {
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
containers/llamacpp/preset.ini
+52
View File
@@ -0,0 +1,52 @@
# Preset per llama-server in modalita' router
#
# Il server gira in router mode (nessun modello fisso): ogni sezione [nome]
# definisce un modello, e il nome della sezione e' il valore da passare nel
# campo "model" dell'API, es. { "model": "qwen36-mtp", "messages": [...] }.
#
# Sezione [*]: parametri di tuning globali condivisi da tutti i modelli.
# I parametri per-modello (es. MTP, ctx-size dedicato) vanno NEL sezione del
# modello, NON in [*].
#
# Hf tag: usa il quant ESATTO del repo (prefisso UD- incluso), es. UD-IQ4_NL,
# altrimenti llama.cpp potrebbe non individuare il file.
#
# Esempi per-modello:
# [chat]
# hf = unsloth/altro-modello-GGUF:UD-Q4_K_M
# ctx-size = 65536
# # chat-template-kwargs = {"thinking":true,"reasoning_effort":"medium"}
version = 1
# Tuning globale (ereditato da ogni modello)
[*]
batch-size = 4096
ubatch-size = 1024
cache-type-k = q4_1
cache-type-v = q4_1
cache-reuse = 256
# MTP nativo (layers MTP nel GGUF): ~1.5-2x piu' veloce.
# Nota: MTP richiede -np 1 (parallel non supportato) e -fa on.
[qwen36-mtp]
hf = unsloth/Qwen3.6-35B-A3B-MTP-GGUF:UD-IQ4_NL
spec-type = draft-mtp
spec-draft-n-max = 2
# Gemma 4 (MTP tramite file draft separato "MTP Q8_0"/"MTP BF16"):
# da configurare esplicitamente con il draft model se serve; senza spec si
# carica senza speculative decoding.
[gemma4]
hf = unsloth/gemma-4-26B-A4B-it-GGUF:UD-IQ4_NL
# DeepSeek V4 Flash: speculative decoding via DSpark (spec-type draft-dspark),
# NON draft-mtp. Il draft module e' un GGUF separato (dspark-...-Q8_0.gguf),
# auto-scaricato da HF tramite hf-repo-draft (il tag :Q8_0 individua univocamente
# il file draft, essendo il solo file "Q8_0" del repo).
[deepseek]
hf = unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-IQ3_XXS
hf-repo-draft = unsloth/DeepSeek-V4-Flash-0731-GGUF:Q8_0
spec-type = draft-dspark
spec-draft-n-max = 3
n-gpu-layers-draft = 99