# LLaMA.cpp HTTP Server

Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**.

Set of LLM REST APIs and a web UI to interact with llama.cpp.

**Features:**
 * LLM inference of F16 and quantized models on GPU and CPU
 * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions, responses, and embeddings routes
 * [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) compatible chat completions
 * Reranking endpoint (https://github.com/ggml-org/llama.cpp/pull/9510)
 * Parallel decoding with multi-user support
 * Continuous batching
 * Multimodal ([documentation](../../docs/multimodal.md)) / with OpenAI-compatible API support
 * Monitoring endpoints
 * Schema-constrained JSON response format
 * Prefilling of assistant messages similar to the Claude API
 * [Function calling](../../docs/function-calling.md) / tool use for ~any model
 * Speculative decoding
 * Easy-to-use web UI

For the full list of features, please refer to [server's changelog](https://github.com/ggml-org/llama.cpp/issues/9291)

## Usage

<!-- HELP_START -->

<!-- IMPORTANT: The list below is auto-generated by llama-gen-docs; do NOT modify it manually -->

### Common params

| Argument | Explanation |
| -------- | ----------- |
| `-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)<br/>(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)<br/>(env: LLAMA_ARG_CTX_SIZE) |
| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity)<br/>(env: LLAMA_ARG_N_PREDICT) |
| `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(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)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)<br/>(env: LLAMA_ARG_SWA_FULL) |
| `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')<br/>(env: LLAMA_ARG_FLASH_ATTN) |
| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)<br/>(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<br/>(env: LLAMA_ARG_ROPE_SCALING_TYPE) |
| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N<br/>(env: LLAMA_ARG_ROPE_SCALE) |
| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)<br/>(env: LLAMA_ARG_ROPE_FREQ_BASE) |
| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N<br/>(env: LLAMA_ARG_ROPE_FREQ_SCALE) |
| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)<br/>(env: LLAMA_ARG_YARN_ORIG_CTX) |
| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.00, 0.0 = full interpolation)<br/>(env: LLAMA_ARG_YARN_EXT_FACTOR) |
| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.00)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.00)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.00)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)<br/>(env: LLAMA_ARG_KV_OFFLOAD) |
| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)<br/>(env: LLAMA_ARG_REPACK) |
| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_NO_HOST) |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(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)<br/>(env: LLAMA_ARG_MMAP) |
| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
| `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
| `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(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<br/>(env: LLAMA_ARG_OVERRIDE_TENSOR) |
| `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU<br/>(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<br/>(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)<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(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<br/>(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)<br/>(env: LLAMA_ARG_MAIN_GPU) |
| `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')<br/>(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<br/>(env: LLAMA_ARG_FIT_TARGET) |
| `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096<br/>(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.<br/>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,...)<br/>note: use comma-separated values |
| `--control-vector FNAME` | add a control vector<br/>note: use comma-separated values to add multiple control vectors |
| `--control-vector-scaled FNAME:SCALE,...` | add a control vector with user defined scaling SCALE<br/>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<br/>(env: LLAMA_ARG_MODEL) |
| `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(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.<br/>example: gemma3<br/>(default: unused)<br/>(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.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
| `-hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(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)<br/>(env: LLAMA_ARG_HF_FILE) |
| `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
| `--log-disable` | Log disable |
| `--log-file FNAME` | Log to file<br/>(env: LLAMA_LOG_FILE) |
| `--log-colors [on\|off\|auto]` | Set colored logging ('on', 'off', or 'auto', default: 'auto')<br/>'auto' enables colors when output is to a terminal<br/>(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<br/>(env: LLAMA_OFFLINE) |
| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:<br/> - 0: generic output<br/> - 1: error<br/> - 2: warning<br/> - 3: info<br/> - 4: debug<br/>(default: 3)<br/><br/>(env: LLAMA_LOG_VERBOSITY) |
| `--log-prefix` | Enable prefix in log messages<br/>(env: LLAMA_LOG_PREFIX) |
| `--log-timestamps` | Enable timestamps in log messages<br/>(env: LLAMA_LOG_TIMESTAMPS) |
| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) |
| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) |


### Sampling params

| Argument | Explanation |
| -------- | ----------- |
| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(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)<br/>(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)<br/>[(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.<br/>(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.<br/>Top K, Nucleus and Locally Typical samplers are ignored if used.<br/>(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,<br/>i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',<br/>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<br/>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<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
| `-bs, --backend-sampling` | enable backend sampling (experimental) (default: disabled)<br/>(env: LLAMA_ARG_BACKEND_SAMPLING) |


### Server-specific params

| Argument | Explanation |
| -------- | ----------- |
| `-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) |
| `-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)<br/>(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)<br/>(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)<br/>(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)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(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<br/>(env: LLAMA_ARG_POOLING) |
| `-np, --parallel N` | number of server slots (default: -1, -1 = auto)<br/>(env: LLAMA_ARG_N_PARALLEL) |
| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
| `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(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)<br/>(env: LLAMA_ARG_MMPROJ_AUTO) |
| `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)<br/>(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)<br/>(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)<br/>(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<br/>(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<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
| `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
| `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
| `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
| `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
| `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)<br/>(env: LLAMA_ARG_REUSE_PORT) |
| `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
| `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
| `--webui-config JSON` | JSON that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG) |
| `--webui-config-file PATH` | JSON file that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG_FILE) |
| `--webui-mcp-proxy, --no-webui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_WEBUI_MCP_PROXY) |
| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)<br/>specify "all" to enable all tools<br/>available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff<br/>(env: LLAMA_ARG_TOOLS) |
| `--webui, --no-webui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_WEBUI) |
| `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
| `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(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)<br/>(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<br/>(env: LLAMA_ARG_SSL_KEY_FILE) |
| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(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"}'<br/>(env: LLAMA_CHAT_TEMPLATE_KWARGS) |
| `-to, --timeout N` | server read/write timeout in seconds (default: 600)<br/>(env: LLAMA_ARG_TIMEOUT) |
| `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
| `--cache-prompt, --no-cache-prompt` | whether to enable prompt caching (default: enabled)<br/>(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)<br/>[(card)](https://ggml.ai/f0.png)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
| `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
| `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
| `--slots, --no-slots` | expose slots monitoring endpoint (default: enabled)<br/>(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)<br/>(env: LLAMA_ARG_MODELS_DIR) |
| `--models-preset PATH` | path to INI file containing model presets for the router server (default: disabled)<br/>(env: LLAMA_ARG_MODELS_PRESET) |
| `--models-max N` | for router server, maximum number of models to load simultaneously (default: 4, 0 = unlimited)<br/>(env: LLAMA_ARG_MODELS_MAX) |
| `--models-autoload, --no-models-autoload` | for router server, whether to automatically load models (default: enabled)<br/>(env: LLAMA_ARG_MODELS_AUTOLOAD) |
| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(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:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
| `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(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)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
| `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>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<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>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<br/>(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)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
| `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>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<br/><br/>(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)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.75)<br/>(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)<br/>(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)<br/>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)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(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)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
| `--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) |

<!-- HELP_END -->

Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.

For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example:
- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled`
- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled`
- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap

Example usage of docker compose with environment variables:

```yml
services:
  llamacpp-server:
    image: ghcr.io/ggml-org/llama.cpp:server
    ports:
      - 8080:8080
    volumes:
      - ./models:/models
    environment:
      # alternatively, you can use "LLAMA_ARG_MODEL_URL" to download the model
      LLAMA_ARG_MODEL: /models/my_model.gguf
      LLAMA_ARG_CTX_SIZE: 4096
      LLAMA_ARG_N_PARALLEL: 2
      LLAMA_ARG_ENDPOINT_METRICS: 1
      LLAMA_ARG_PORT: 8080
```

### Multimodal support

Multimodal support was added in [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) and is currently an experimental feature.
It is currently available in the following endpoints:
- The OAI-compatible chat endpoint.
- The non-OAI-compatible completions endpoint.
- The non-OAI-compatible embeddings endpoint.

For more details, please refer to [multimodal documentation](../../docs/multimodal.md)

### Built-in tools support

The server includes a set of built-in tools that enable the LLM to access the local file system directly from the Web UI.

To use this feature, start the server with `--tools all`. You can also enable only specific tools by passing a comma-separated list: `--tools name1,name2,...`. Run `--help` for the full list of available tool names.

## Build

`llama-server` is built alongside everything else from the root of the project

- Using `CMake`:

  ```bash
  cmake -B build
  cmake --build build --config Release -t llama-server
  ```

  Binary is at `./build/bin/llama-server`

## Build with SSL

`llama-server` can also be built with SSL support using OpenSSL 3

- Using `CMake`:

  ```bash
  cmake -B build -DLLAMA_OPENSSL=ON
  cmake --build build --config Release -t llama-server
  ```

## Quick Start

To get started right away, run the following command, making sure to use the correct path for the model you have:

### Unix-based systems (Linux, macOS, etc.)

```bash
./llama-server -m models/7B/ggml-model.gguf -c 2048
```

### Windows

```powershell
llama-server.exe -m models\7B\ggml-model.gguf -c 2048
```

The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.

### Docker

```bash
docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080

# or, with CUDA:
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggml-org/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
```

## Using with CURL

Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.

```sh
curl --request POST \
    --url http://localhost:8080/completion \
    --header "Content-Type: application/json" \
    --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
```

## API Endpoints

### GET `/health`: Returns health check result

This endpoint is public (no API key check). `/v1/health` also works.

**Response format**

- HTTP status code 503
  - Body: `{"error": {"code": 503, "message": "Loading model", "type": "unavailable_error"}}`
  - Explanation: the model is still being loaded.
- HTTP status code 200
  - Body: `{"status": "ok" }`
  - Explanation: the model is successfully loaded and the server is ready.

### POST `/completion`: Given a `prompt`, it returns the predicted completion.

> [!IMPORTANT]
>
> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/completions` instead.

*Options:*

`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:

  - The prompt is a string or an array with the first element given as a string
  - The model's `tokenizer.ggml.add_bos_token` metadata is `true`

These input shapes and data type are allowed for `prompt`:

  - Single string: `"string"`
  - Single sequence of tokens: `[12, 34, 56]`
  - Mixed tokens and strings: `[12, 34, "string", 56, 78]`
  - A JSON object which optionally contains multimodal data: `{ "prompt_string": "string", "multimodal_data": ["base64"] }`

Multiple prompts are also supported. In this case, the completion result will be an array.

  - Only strings: `["string1", "string2"]`
  - Strings, JSON objects, and sequences of tokens: `["string1", [12, 34, 56], { "prompt_string": "string", "multimodal_data": ["base64"]}]`
  - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string", { "prompt_string": "string" }]`

Note for `multimodal_data` in JSON object prompts. This should be an array of strings, containing base64 encoded multimodal data such as images and audio. There must be an identical number of MTMD media markers in the string prompt element which act as placeholders for the data provided to this parameter. The multimodal data files will be substituted in order. The marker string (e.g. `<__media__>`) can be found by calling `mtmd_default_marker()` defined in [the MTMD C API](https://github.com/ggml-org/llama.cpp/blob/5fd160bbd9d70b94b5b11b0001fd7f477005e4a0/tools/mtmd/mtmd.h#L87). A client *must not* specify this field unless the server has the multimodal capability. Clients should check `/models` or `/v1/models` for the `multimodal` capability before a multimodal request.

`temperature`: Adjust the randomness of the generated text. Default: `0.8`

`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.

`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`

`top_k`: Limit the next token selection to the K most probable tokens.  Default: `40`

`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`

`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`

`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.

`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0`

`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.

`n_cmpl`: Number of completions to generate from the current prompt. If input has multiple prompts, the output will have N prompts times `n_cmpl` entries.

`n_cache_reuse`: Min chunk size to attempt reusing from the cache via KV shifting. For more info, see `--cache-reuse` arg. Default: `0`, which is disabled.

`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.

`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`

`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.

`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`

`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.

`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.

`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.

`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled.

`dry_base`: Set the DRY repetition penalty base value. Default: `1.75`

`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2`

`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size.

`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`

`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled.

`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC)

`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.

`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`

`mirostat_eta`: Set the Mirostat learning rate, parameter eta.  Default: `0.1`

`grammar`: Set grammar for grammar-based sampling.  Default: no grammar

`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features.  Default: no JSON schema.

`seed`: Set the random number generator (RNG) seed.  Default: `-1`, which is a random seed.

`ignore_eos`: Ignore end of stream token and continue generating.  Default: `false`

`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. For compatibility with the OpenAI API, a JSON object {"<string or token id>": bias, ...} can also be passed. Default: `[]`

`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0`

`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`

`t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled.

`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`

`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`

`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false`

`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.

`timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`

`return_progress`: Include prompt processing progress in `stream` mode. The progress will be contained inside `prompt_progress` with 4 values: `total`, `cache`, `processed`, and `time_ms`. The overall progress is `processed/total`, while the actual timed progress is `(processed-cache)/(total-cache)`. The `time_ms` field contains the elapsed time in milliseconds since prompt processing started. Default: `false`

`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.

`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name.

`lora`: A list of LoRA adapters to be applied to this specific request. Each object in the list must contain `id` and `scale` fields. For example: `[{"id": 0, "scale": 0.5}, {"id": 1, "scale": 1.1}]`. If a LoRA adapter is not specified in the list, its scale will default to `0.0`. Please note that requests with different LoRA configurations will not be batched together, which may result in performance degradation.

**Response format**

- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.

- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements:
  ```
  {
    "content": "<the generated completion text>",
    "tokens": [ generated token ids if requested ],
    ...
    "probs": [
      {
        "id": <token id>,
        "logprob": float,
        "token": "<most likely token>",
        "bytes": [int, int, ...],
        "top_logprobs": [
          {
            "id": <token id>,
            "logprob": float,
            "token": "<token text>",
            "bytes": [int, int, ...],
          },
          {
            "id": <token id>,
            "logprob": float,
            "token": "<token text>",
            "bytes": [int, int, ...],
          },
          ...
        ]
      },
      {
        "id": <token id>,
        "logprob": float,
        "token": "<most likely token>",
        "bytes": [int, int, ...],
        "top_logprobs": [
          ...
        ]
      },
      ...
    ]
  },
  ```
  Please note that if `post_sampling_probs` is set to `true`:
    - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0
    - `top_logprobs` will be replaced with `top_probs`. Each element contains:
      - `id`: token ID
      - `token`: token in string
      - `bytes`: token in bytes
      - `prob`: token probability, with the value between 0.0 and 1.0
    - Number of elements in `top_probs` may be less than `n_probs`

- `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request.
- `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
- `model`: The model alias (for model path, please use `/props` endpoint)
- `prompt`: The processed `prompt` (special tokens may be added)
- `stop_type`: Indicating whether the completion has stopped. Possible values are:
  - `none`: Generating (not stopped)
  - `eos`: Stopped because it encountered the EOS token
  - `limit`: Stopped because `n_predict` tokens were generated before stop words or EOS was encountered
  - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided
- `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
- `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
- `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion
- `tokens_evaluated`: Number of tokens evaluated in total from the prompt
- `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)


### POST `/tokenize`: Tokenize a given text

*Options:*

`content`: (Required) The text to tokenize.

`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`

`parse_special`: (Optional) Boolean indicating if special tokens should be tokenized. When `false` special tokens are treated as plaintext.  Default: `true`

`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs.  Default: `false`

**Response:**

Returns a JSON object with a `tokens` field containing the tokenization result. The `tokens` array contains either just token IDs or objects with `id` and `piece` fields, depending on the `with_pieces` parameter. The piece field is a string if the piece is valid unicode or a list of bytes otherwise.


If `with_pieces` is `false`:
```json
{
  "tokens": [123, 456, 789]
}
```

If `with_pieces` is `true`:
```json
{
  "tokens": [
    {"id": 123, "piece": "Hello"},
    {"id": 456, "piece": " world"},
    {"id": 789, "piece": "!"}
  ]
}
```

With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
```
{
  "tokens": [
    {"id": 198, "piece": [195]}, // hex C3
    {"id": 164, "piece": [161]} // hex A1
  ]
}
```

### POST `/detokenize`: Convert tokens to text

*Options:*

`tokens`: Set the tokens to detokenize.

### POST `/apply-template`: Apply chat template to a conversation

Uses the server's prompt template formatting functionality to convert chat messages to a single string expected by a chat model as input, but does not perform inference. Instead, the prompt string is returned in the `prompt` field of the JSON response. The prompt can then be modified as desired (for example, to insert "Sure!" at the beginning of the model's response) before sending to `/completion` to generate the chat response.

*Options:*

`messages`: (Required) Chat turns in the same format as `/v1/chat/completions`.

**Response format**

Returns a JSON object with a field `prompt` containing a string of the input messages formatted according to the model's chat template format.

### POST `/embedding`: Generate embedding of a given text

> [!IMPORTANT]
>
> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/embeddings` instead.

The same as [the embedding example](../embedding) does.

This endpoint also supports multimodal embeddings. See the documentation for the `/completions` endpoint for details on how to send a multimodal prompt.

*Options:*

`content`: Set the text to process.

`embd_normalize`: Normalization for pooled embeddings. Can be one of the following values:
```
  -1: No normalization
   0: Max absolute
   1: Taxicab
   2: Euclidean/L2
  >2: P-Norm
```

### POST `/reranking`: Rerank documents according to a given query

Similar to https://jina.ai/reranker/ but might change in the future.
Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)) and the `--embedding --pooling rank` options.

*Options:*

`query`: The query against which the documents will be ranked.

`documents`: An array strings representing the documents to be ranked.

*Aliases:*
  - `/rerank`
  - `/v1/rerank`
  - `/v1/reranking`

*Examples:*

```shell
curl http://127.0.0.1:8012/v1/rerank \
    -H "Content-Type: application/json" \
    -d '{
        "model": "some-model",
            "query": "What is panda?",
            "top_n": 3,
            "documents": [
                "hi",
            "it is a bear",
            "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
            ]
    }' | jq
```

### POST `/infill`: For code infilling.

Takes a prefix and a suffix and returns the predicted completion as stream.

*Options:*

- `input_prefix`: Set the prefix of the code to infill.
- `input_suffix`: Set the suffix of the code to infill.
- `input_extra`:  Additional context inserted before the FIM prefix.
- `prompt`:       Added after the `FIM_MID` token

`input_extra` is array of `{"filename": string, "text": string}` objects.

The endpoint also accepts all the options of `/completion`.

If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used:

```txt
<FIM_REP>myproject
<FIM_SEP>{chunk 0 filename}
{chunk 0 text}
<FIM_SEP>{chunk 1 filename}
{chunk 1 text}
...
<FIM_SEP>filename
<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
```

If the tokens are missing, then the extra context is simply prefixed at the start:

```txt
[input_extra]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
```

### **GET** `/props`: Get server global properties.

By default, it is read-only. To make POST request to change global properties, you need to start server with `--props`

**Response format**

```json
{
  "default_generation_settings": {
    "id": 0,
    "id_task": -1,
    "n_ctx": 1024,
    "speculative": false,
    "is_processing": false,
    "params": {
      "n_predict": -1,
      "seed": 4294967295,
      "temperature": 0.800000011920929,
      "dynatemp_range": 0.0,
      "dynatemp_exponent": 1.0,
      "top_k": 40,
      "top_p": 0.949999988079071,
      "min_p": 0.05000000074505806,
      "xtc_probability": 0.0,
      "xtc_threshold": 0.10000000149011612,
      "typical_p": 1.0,
      "repeat_last_n": 64,
      "repeat_penalty": 1.0,
      "presence_penalty": 0.0,
      "frequency_penalty": 0.0,
      "dry_multiplier": 0.0,
      "dry_base": 1.75,
      "dry_allowed_length": 2,
      "dry_penalty_last_n": -1,
      "dry_sequence_breakers": [
        "\n",
        ":",
        "\"",
        "*"
      ],
      "mirostat": 0,
      "mirostat_tau": 5.0,
      "mirostat_eta": 0.10000000149011612,
      "stop": [],
      "max_tokens": -1,
      "n_keep": 0,
      "n_discard": 0,
      "ignore_eos": false,
      "stream": true,
      "n_probs": 0,
      "min_keep": 0,
      "grammar": "",
      "samplers": [
        "dry",
        "top_k",
        "typ_p",
        "top_p",
        "min_p",
        "xtc",
        "temperature"
      ],
      "speculative.n_max": 16,
      "speculative.n_min": 5,
      "speculative.p_min": 0.8999999761581421,
      "timings_per_token": false
    },
    "prompt": "",
    "next_token": {
      "has_next_token": true,
      "has_new_line": false,
      "n_remain": -1,
      "n_decoded": 0,
      "stopping_word": ""
    }
  },
  "total_slots": 1,
  "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
  "chat_template": "...",
  "chat_template_caps": {},
  "modalities": {
    "vision": false
  },
  "build_info": "b(build number)-(build commit hash)",
  "is_sleeping": false
}
```

- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- `model_path` - the path to model file (same with `-m` argument)
- `chat_template` - the model's original Jinja2 prompt template
- `chat_template_caps` - capabilities of the chat template (see `common/jinja/caps.h` for more info)
- `modalities` - the list of supported modalities
- `is_sleeping` - sleeping status, see [Sleeping on idle](#sleeping-on-idle)

### POST `/props`: Change server global properties.

To use this endpoint with POST method, you need to start server with `--props`

*Options:*

- None yet

### POST `/embeddings`: non-OpenAI-compatible embeddings API

This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidean norm.

Note that the response format of this endpoint is different from `/v1/embeddings`.

*Options:*

Same as the `/v1/embeddings` endpoint.

*Examples:*

Same as the `/v1/embeddings` endpoint.

**Response format**

```
[
  {
    "index": 0,
    "embedding": [
      [ ... embeddings for token 0   ... ],
      [ ... embeddings for token 1   ... ],
      [ ... ]
      [ ... embeddings for token N-1 ... ],
    ]
  },
  ...
  {
    "index": P,
    "embedding": [
      [ ... embeddings for token 0   ... ],
      [ ... embeddings for token 1   ... ],
      [ ... ]
      [ ... embeddings for token N-1 ... ],
    ]
  }
]
```

### GET `/slots`: Returns the current slots processing state

This endpoint is enabled by default and can be disabled with `--no-slots`. It can be used to query various per-slot metrics, such as speed, processed tokens, sampling parameters, etc.

If query param `?fail_on_no_slot=1` is set, this endpoint will respond with status code 503 if there is no available slots.

**Response format**

<details>
<summary>Example with 2 slots</summary>

```json
[
  {
    "id": 0,
    "id_task": 135,
    "n_ctx": 65536,
    "speculative": false,
    "is_processing": true,
    "params": {
      "n_predict": -1,
      "seed": 4294967295,
      "temperature": 0.800000011920929,
      "dynatemp_range": 0.0,
      "dynatemp_exponent": 1.0,
      "top_k": 40,
      "top_p": 0.949999988079071,
      "min_p": 0.05000000074505806,
      "top_n_sigma": -1.0,
      "xtc_probability": 0.0,
      "xtc_threshold": 0.10000000149011612,
      "typical_p": 1.0,
      "repeat_last_n": 64,
      "repeat_penalty": 1.0,
      "presence_penalty": 0.0,
      "frequency_penalty": 0.0,
      "dry_multiplier": 0.0,
      "dry_base": 1.75,
      "dry_allowed_length": 2,
      "dry_penalty_last_n": 131072,
      "mirostat": 0,
      "mirostat_tau": 5.0,
      "mirostat_eta": 0.10000000149011612,
      "max_tokens": -1,
      "n_keep": 0,
      "n_discard": 0,
      "ignore_eos": false,
      "stream": true,
      "n_probs": 0,
      "min_keep": 0,
      "chat_format": "GPT-OSS",
      "reasoning_format": "none",
      "reasoning_in_content": false,
      "generation_prompt": "",
      "samplers": [
        "penalties",
        "dry",
        "top_k",
        "typ_p",
        "top_p",
        "min_p",
        "xtc",
        "temperature"
      ],
      "speculative.n_max": 16,
      "speculative.n_min": 0,
      "speculative.p_min": 0.75,
      "timings_per_token": false,
      "post_sampling_probs": false,
      "lora": []
    },
    "next_token": {
      "has_next_token": true,
      "has_new_line": false,
      "n_remain": -1,
      "n_decoded": 0
    }
  },
  {
    "id": 1,
    "id_task": 0,
    "n_ctx": 65536,
    "speculative": false,
    "is_processing": true,
    "params": {
      "n_predict": -1,
      "seed": 4294967295,
      "temperature": 0.800000011920929,
      "dynatemp_range": 0.0,
      "dynatemp_exponent": 1.0,
      "top_k": 40,
      "top_p": 0.949999988079071,
      "min_p": 0.05000000074505806,
      "top_n_sigma": -1.0,
      "xtc_probability": 0.0,
      "xtc_threshold": 0.10000000149011612,
      "typical_p": 1.0,
      "repeat_last_n": 64,
      "repeat_penalty": 1.0,
      "presence_penalty": 0.0,
      "frequency_penalty": 0.0,
      "dry_multiplier": 0.0,
      "dry_base": 1.75,
      "dry_allowed_length": 2,
      "dry_penalty_last_n": 131072,
      "mirostat": 0,
      "mirostat_tau": 5.0,
      "mirostat_eta": 0.10000000149011612,
      "max_tokens": -1,
      "n_keep": 0,
      "n_discard": 0,
      "ignore_eos": false,
      "stream": true,
      "n_probs": 0,
      "min_keep": 0,
      "chat_format": "GPT-OSS",
      "reasoning_format": "none",
      "reasoning_in_content": false,
      "generation_prompt": "",
      "samplers": [
        "penalties",
        "dry",
        "top_k",
        "typ_p",
        "top_p",
        "min_p",
        "xtc",
        "temperature"
      ],
      "speculative.n_max": 16,
      "speculative.n_min": 0,
      "speculative.p_min": 0.75,
      "timings_per_token": false,
      "post_sampling_probs": false,
      "lora": []
    },
    "next_token": {
      "has_next_token": true,
      "has_new_line": true,
      "n_remain": -1,
      "n_decoded": 136
    }
  }
]
```

</details>

### GET `/metrics`: Prometheus compatible metrics exporter

This endpoint is only accessible if `--metrics` is set.

Available metrics:
- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
- `llamacpp:kv_cache_tokens`: KV-cache tokens.
- `llamacpp:requests_processing`: Number of requests processing.
- `llamacpp:requests_deferred`: Number of requests deferred.
- `llamacpp:n_tokens_max`: High watermark of the context size observed.

### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.

*Options:*

`filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.

**Response format**

```json
{
    "id_slot": 0,
    "filename": "slot_save_file.bin",
    "n_saved": 1745,
    "n_written": 14309796,
    "timings": {
        "save_ms": 49.865
    }
}
```

### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.

*Options:*

`filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.

**Response format**

```json
{
    "id_slot": 0,
    "filename": "slot_save_file.bin",
    "n_restored": 1745,
    "n_read": 14309796,
    "timings": {
        "restore_ms": 42.937
    }
}
```

### POST `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.

**Response format**

```json
{
    "id_slot": 0,
    "n_erased": 1745
}
```

### GET `/lora-adapters`: Get list of all LoRA adapters

This endpoint returns the loaded LoRA adapters. You can add adapters using `--lora` when starting the server, for example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...`

By default, all adapters will be loaded with scale set to 1. To initialize all adapters scale to 0, add `--lora-init-without-apply`

Please note that this value will be overwritten by the `lora` field for each request.

If an adapter is disabled, the scale will be set to 0.

**Response format**

```json
[
    {
        "id": 0,
        "path": "my_adapter_1.gguf",
        "scale": 0.0
    },
    {
        "id": 1,
        "path": "my_adapter_2.gguf",
        "scale": 0.0
    }
]
```

### POST `/lora-adapters`: Set list of LoRA adapters

This sets the global scale for LoRA adapters. Please note that this value will be overwritten by the `lora` field for each request.

To disable an adapter, either remove it from the list below, or set scale to 0.

**Request format**

To know the `id` of the adapter, use GET `/lora-adapters`

```json
[
  {"id": 0, "scale": 0.2},
  {"id": 1, "scale": 0.8}
]
```

## OpenAI-compatible API Endpoints

### GET `/v1/models`: OpenAI-compatible Model Info API

Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models).

The returned list always has one single element. The `meta` field can be `null` (for example, while the model is still loading).

By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`.

Example:

```json
{
    "object": "list",
    "data": [
        {
            "id": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
            "object": "model",
            "created": 1735142223,
            "owned_by": "llamacpp",
            "meta": {
                "vocab_type": 2,
                "n_vocab": 128256,
                "n_ctx_train": 131072,
                "n_embd": 4096,
                "n_params": 8030261312,
                "size": 4912898304
            }
        }
    ]
}
```

### POST `/v1/completions`: OpenAI-compatible Completions API

Given an input `prompt`, it returns the predicted completion. Streaming mode is also supported. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps.

*Options:*

See [OpenAI Completions API documentation](https://platform.openai.com/docs/api-reference/completions).

llama.cpp `/completion`-specific features such as `mirostat` are supported.

*Examples:*

Example usage with `openai` python library:

```python
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
    api_key = "sk-no-key-required"
)

completion = client.completions.create(
  model="davinci-002",
  prompt="I believe the meaning of life is",
  max_tokens=8
)

print(completion.choices[0].text)
```

### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API

Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.

If model supports multimodal, you can input the media file via `image_url` content part. We support both base64 and remote URL as input. See OAI documentation for more.

*Options:*

See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also supported.

The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.

`chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}`

`reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text.

`generation_prompt`: The generation prompt that was prefilled in by the template. Prepended to model output before parsing.

`parse_tool_calls`: Whether to parse the generated tool call.

`parallel_tool_calls` : Whether to enable parallel/multiple tool calls (only supported on some models, verification is based on jinja template).

*Examples:*

You can use either Python `openai` library with appropriate checkpoints:

```python
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
    api_key = "sk-no-key-required"
)

completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
    {"role": "user", "content": "Write a limerick about python exceptions"}
  ]
)

print(completion.choices[0].message)
```

... or raw HTTP requests:

```shell
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
    "role": "system",
    "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
},
{
    "role": "user",
    "content": "Write a limerick about python exceptions"
}
]
}'
```

*Tool call support*

[OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) is supported with the `--jinja` flag (and may require a `--chat-template-file` override to get the right tool-use compatible Jinja template; worst case, `--chat-template chatml` may also work).

**See our [Function calling](../../docs/function-calling.md) docs** for more details, supported native tool call styles (generic tool call style is used as fallback) / examples of use.

*Timings and context usage*

The response contains a `timings` object, for example:

```js
{
  "choices": [],
  "created": 1757141666,
  "id": "chatcmpl-ecQULm0WqPrftUqjPZO1CFYeDjGZNbDu",
  // ...
  "timings": {
    "cache_n": 236, // number of prompt tokens reused from cache
    "prompt_n": 1, // number of prompt tokens being processed
    "prompt_ms": 30.958,
    "prompt_per_token_ms": 30.958,
    "prompt_per_second": 32.301828283480845,
    "predicted_n": 35, // number of predicted tokens
    "predicted_ms": 661.064,
    "predicted_per_token_ms": 18.887542857142858,
    "predicted_per_second": 52.94494935437416
  }
}
```

This provides information on the performance of the server. It also allows calculating the current context usage.

The total number of tokens in context is equal to `prompt_n + cache_n + predicted_n`

*Reasoning support*

The server supports parsing and returning reasoning via the `reasoning_content` field, similar to Deepseek API.

Reasoning input (preserve reasoning in history) is also supported by some specific templates. For more details, please refer to [PR#18994](https://github.com/ggml-org/llama.cpp/pull/18994).

### POST `/v1/responses`: OpenAI-compatible Responses API

*Options:*

See [OpenAI Responses API documentation](https://platform.openai.com/docs/api-reference/responses).

*Examples:*

You can use either Python `openai` library with appropriate checkpoints:

```python
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
    api_key = "sk-no-key-required"
)

response = client.responses.create(
  model="gpt-4.1",
  instructions="You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.",
  input="Write a limerick about python exceptions"
)

print(response.output_text)
```

... or raw HTTP requests:

```shell
curl http://localhost:8080/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"model": "gpt-4.1",
"instructions": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.",
"input": "Write a limerick about python exceptions"
}'
```

This endpoint works by converting Responses request into Chat Completions request.


### POST `/v1/embeddings`: OpenAI-compatible embeddings API

This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.

*Options:*

See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).

*Examples:*

- input as string

  ```shell
  curl http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer no-key" \
  -d '{
          "input": "hello",
          "model":"GPT-4",
          "encoding_format": "float"
  }'
  ```

- `input` as string array

  ```shell
  curl http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer no-key" \
  -d '{
          "input": ["hello", "world"],
          "model":"GPT-4",
          "encoding_format": "float"
  }'
  ```

### POST `/v1/messages`: Anthropic-compatible Messages API

Given a list of `messages`, returns the assistant's response. Streaming is supported via Server-Sent Events. While no strong claims of compatibility with the Anthropic API spec are made, in our experience it suffices to support many apps.

*Options:*

See [Anthropic Messages API documentation](https://docs.anthropic.com/en/api/messages). Tool use requires `--jinja` flag.

`model`: Model identifier (required)

`messages`: Array of message objects with `role` and `content` (required)

`max_tokens`: Maximum tokens to generate (default: 4096)

`system`: System prompt as string or array of content blocks

`temperature`: Sampling temperature 0-1 (default: 1.0)

`top_p`: Nucleus sampling (default: 1.0)

`top_k`: Top-k sampling

`stop_sequences`: Array of stop sequences

`stream`: Enable streaming (default: false)

`tools`: Array of tool definitions (requires `--jinja`)

`tool_choice`: Tool selection mode (`{"type": "auto"}`, `{"type": "any"}`, or `{"type": "tool", "name": "..."}`)

*Examples:*

```shell
curl http://localhost:8080/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key" \
  -d '{
    "model": "gpt-4",
    "max_tokens": 1024,
    "system": "You are a helpful assistant.",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'
```

### POST `/v1/messages/count_tokens`: Token Counting

Counts the number of tokens in a request without generating a response.

Accepts the same parameters as `/v1/messages`. The `max_tokens` parameter is not required.

*Example:*

```shell
curl http://localhost:8080/v1/messages/count_tokens \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'
```

*Response:*

```json
{"input_tokens": 10}
```

## Server built-in tools

The server exposes a REST API under `/tools` that allows the Web UI to call built-in tools. This endpoint is intended to be used internally by the Web UI and subject to change or to be removed in the future.

**Please do NOT use this endpoint in a downstream application**

For further documentation about this endpoint, please refer to [server internal documentation](./README-dev.md)

## Using multiple models

`llama-server` can be launched in a **router mode** that exposes an API for dynamically loading and unloading models. The main process (the "router") automatically forwards each request to the appropriate model instance.

To start in router mode, launch `llama-server` **without specifying any model**:

```sh
llama-server
```

### Model sources

There are 3 possible sources for model files:
1. Cached models (controlled by the `LLAMA_CACHE` environment variable)
2. Custom model directory (set via the `--models-dir` argument)
3. Custom preset (set via the `--models-preset` argument)

By default, the router looks for models in the cache. You can add Hugging Face models to the cache with:

```sh
llama-server -hf <user>/<model>:<tag>
```

*The server must be restarted after adding a new model.*

Alternatively, you can point the router to a local directory containing your GGUF files using `--models-dir`. Example command:

```sh
llama-server --models-dir ./models_directory
```

If the model contains multiple GGUF (for multimodal or multi-shard), files should be put into a subdirectory. The directory structure should look like this:

```sh
models_directory
 │
 │  # single file
 ├─ llama-3.2-1b-Q4_K_M.gguf
 ├─ Qwen3-8B-Q4_K_M.gguf
 │
 │  # multimodal
 ├─ gemma-3-4b-it-Q8_0
 │    ├─ gemma-3-4b-it-Q8_0.gguf
 │    └─ mmproj-F16.gguf   # file name must start with "mmproj"
 │
 │  # multi-shard
 ├─ Kimi-K2-Thinking-UD-IQ1_S
 │    ├─ Kimi-K2-Thinking-UD-IQ1_S-00001-of-00006.gguf
 │    ├─ Kimi-K2-Thinking-UD-IQ1_S-00002-of-00006.gguf
 │    ├─ ...
 │    └─ Kimi-K2-Thinking-UD-IQ1_S-00006-of-00006.gguf
```

You may also specify default arguments that will be passed to every model instance:

```sh
llama-server -ctx 8192 -n 1024 -np 2
```

Note: model instances inherit both command line arguments and environment variables from the router server.

Alternatively, you can also add GGUF based preset (see next section)

### Model presets

Model presets allow advanced users to define custom configurations using an `.ini` file:

```sh
llama-server --models-preset ./my-models.ini
```

Each section in the file defines a new preset. Keys within a section correspond to command-line arguments (without leading dashes). For example, the argument `--n-gpu-layers 123` is written as `n-gpu-layers = 123`.

Short argument forms (e.g., `c`, `ngl`) and environment variable names (e.g., `LLAMA_ARG_N_GPU_LAYERS`) are also supported as keys.

Example:

```ini
version = 1

; (Optional) This section provides global settings shared across all presets.
; If the same key is defined in a specific preset, it will override the value in this global section.
[*]
c = 8192
n-gpu-layers = 8

; If the key corresponds to an existing model on the server,
; this will be used as the default config for that model
[ggml-org/MY-MODEL-GGUF:Q8_0]
; string value
chat-template = chatml
; numeric value
n-gpu-layers = 123
; flag value (for certain flags, you need to use the "no-" prefix for negation)
jinja = true
; shorthand argument (for example, context size)
c = 4096
; environment variable name
LLAMA_ARG_CACHE_RAM = 0
; file paths are relative to server's CWD
model-draft = ./my-models/draft.gguf
; but it's RECOMMENDED to use absolute path
model-draft = /Users/abc/my-models/draft.gguf

; If the key does NOT correspond to an existing model,
; you need to specify at least the model path or HF repo
[custom_model]
model = /Users/abc/my-awesome-model-Q4_K_M.gguf
```

Note: some arguments are controlled by router (e.g., host, port, API key, HF repo, model alias). They will be removed or overwritten upon loading.

The precedence rule for preset options is as follows:
1. **Command-line arguments** passed to `llama-server` (highest priority)
2. **Model-specific options** defined in the preset file (e.g. `[ggml-org/MY-MODEL...]`)
3. **Global options** defined in the preset file (`[*]`)

We also offer additional options that are exclusive to presets (these aren't treated as command-line arguments):
- `load-on-startup` (boolean): Controls whether the model loads automatically when the server starts
- `stop-timeout` (int, seconds): After requested unload, wait for this many seconds before forcing termination (default: 10)

### Routing requests

Requests are routed according to the requested model name.

For **POST** endpoints (`/v1/chat/completions`, `/v1/completions`, `/infill`, etc.) The router uses the `"model"` field in the JSON body:

```json
{
  "model": "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M",
  "messages": [
    {
      "role": "user",
      "content": "hello"
    }
  ]
}
```

For **GET** endpoints (`/props`, `/metrics`, etc.) The router uses the `model` query parameter (URL-encoded):

```
GET /props?model=ggml-org%2Fgemma-3-4b-it-GGUF%3AQ4_K_M
```

By default, the model will be loaded automatically if it's not loaded. To disable this, add `--no-models-autoload` when starting the server. Additionally, you can include `?autoload=true|false` in the query param to control this behavior per-request.

### GET `/models`: List available models

Listing all models in cache. The model metadata will also include a field to indicate the status of the model:

```json
{
  "data": [{
    "id": "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M",
    "in_cache": true,
    "path": "/Users/REDACTED/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_gemma-3-4b-it-Q4_K_M.gguf",
    "status": {
      "value": "loaded",
      "args": ["llama-server", "-ctx", "4096"]
    },
    ...
  }]
}
```

Note: For a local GGUF (stored offline in a custom directory), the model object will have `"in_cache": false`.

The `status` object can be:

```json
"status": {
  "value": "unloaded"
}
```

```json
"status": {
  "value": "loading",
  "args": ["llama-server", "-ctx", "4096"]
}
```

```json
"status": {
  "value": "unloaded",
  "args": ["llama-server", "-ctx", "4096"],
  "failed": true,
  "exit_code": 1
}
```

```json
"status": {
  "value": "loaded",
  "args": ["llama-server", "-ctx", "4096"]
}
```

```json
"status": {
  "value": "sleeping",
  "args": ["llama-server", "-ctx", "4096"]
}
```

### POST `/models/load`: Load a model

Load a model

Payload:
- `model`: name of the model to be loaded.

```json
{
  "model": "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
}
```

Response:

```json
{
  "success": true
}
```


### POST `/models/unload`: Unload a model

Unload a model

Payload:

```json
{
  "model": "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M",
}
```

Response:

```json
{
  "success": true
}
```

## API errors

`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi

Example of an error:

```json
{
    "error": {
        "code": 401,
        "message": "Invalid API Key",
        "type": "authentication_error"
    }
}
```

## Sleeping on Idle

The server supports an automatic sleep mode that activates after a specified period of inactivity (no incoming tasks). This feature, introduced in [PR #18228](https://github.com/ggml-org/llama.cpp/pull/18228), can be enabled using the `--sleep-idle-seconds` command-line argument. It works seamlessly in both single-model and multi-model configurations.

When the server enters sleep mode, the model and its associated memory (including the KV cache) are unloaded from RAM to conserve resources. Any new incoming task will automatically trigger the model to reload.

The sleeping status can be retrieved from the `GET /props` endpoint (or `/props?model=(model_name)` in router mode).

Note that the following endpoints are exempt from being considered as incoming tasks. They do not trigger model reloading and do not reset the idle timer:
- `GET /health`
- `GET /props`
- `GET /models`

## More examples

### Interactive mode

Check the sample in [chat.mjs](chat.mjs).
Run with NodeJS version 16 or later:

```sh
node chat.mjs
```

Another sample in [chat.sh](chat.sh).
Requires [bash](https://www.gnu.org/software/bash/), [curl](https://curl.se) and [jq](https://jqlang.github.io/jq/).
Run with bash:

```sh
bash chat.sh
```

Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:

**When /metrics or /slots endpoint is disabled**

```json
{
    "error": {
        "code": 501,
        "message": "This server does not support metrics endpoint.",
        "type": "not_supported_error"
    }
}
```

**When the server receives invalid grammar via */completions endpoint**

```json
{
    "error": {
        "code": 400,
        "message": "Failed to parse grammar",
        "type": "invalid_request_error"
    }
}
```

### Custom default Web UI preferences

You can specify default preferences for the web UI using `--webui-config <JSON config>` or `--webui-config-file <path to JSON config>`. For example, you can disable pasting long text as attachments and enable rendering Markdown in user messages with this command:

```bash
./llama-server -m model.gguf --webui-config '{"pasteLongTextToFileLen": 0, "renderUserContentAsMarkdown": true}'
```

You may find available preferences in [settings-config.ts](webui/src/lib/constants/settings-config.ts).

### Legacy completion web UI

A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggml-org/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./tools/server/public_legacy`

For example:

```sh
./llama-server -m my_model.gguf -c 8192 --path ./tools/server/public_legacy
```

### Extending or building alternative Web Front End

You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.

Read the documentation in `/completion.js` to see convenient ways to access llama.

A simple example is below:

```html
<html>
  <body>
    <pre>
      <script type="module">
        import { llama } from '/completion.js'

        const prompt = `### Instruction:
Write dad jokes, each one paragraph.
You can use html formatting if needed.

### Response:`

        for await (const chunk of llama(prompt)) {
          document.write(chunk.data.content)
        }
      </script>
    </pre>
  </body>
</html>
```
