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**.
| `--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) |
| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
| `--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 |
| `-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,tensor}` | 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 (pipelined)<br/>- row: split weight across GPUs by rows (parallelized)<br/>- tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL)<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) |
| `-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) |
| `--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 |
| `--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,...) |
| `-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) |
| `-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) |
| `--log-file FNAME` | Log to file<br/>(env: LLAMA_ARG_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_ARG_LOG_COLORS) |
| `--spec-draft-type-k, -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_SPEC_DRAFT_CACHE_TYPE_K) |
| `--spec-draft-type-v, -ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model<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_SPEC_DRAFT_CACHE_TYPE_V) |
| `--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) |
| `--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) |
| `-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' |
| `-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 |
| `-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) |
| `-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) |
| `--cache-idle-slots, --no-cache-idle-slots` | save idle slots to the prompt cache on new task, and clear them when using unified KV (default: enabled, requires cache-ram)<br/>(env: LLAMA_ARG_CACHE_IDLE_SLOTS) |
| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
| `-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) |
| `--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) |
| `--mtmd-batch-max-tokens N` | maximum number of image tokens per batch when encoding images (default: 1024)<br/>(env: LLAMA_ARG_MTMD_BATCH_MAX_TOKENS) |
| `--cors-origins ORIGINS` | comma-separated list of allowed origins for CORS (default: *)<br/>if set to special value 'localhost', reflect the Origin header only if it is localhost<br/>(env: LLAMA_ARG_CORS_ORIGINS) |
| `--cors-methods METHODS` | comma-separated list of allowed methods for CORS (default: GET, POST, DELETE, OPTIONS)<br/>(env: LLAMA_ARG_CORS_METHODS) |
| `--cors-headers HEADERS` | comma-separated list of allowed headers for CORS (default: *)<br/>(env: LLAMA_ARG_CORS_HEADERS) |
| `--cors-credentials, --no-cors-credentials` | whether to allow credentials for CORS (default: enabled)<br/>note: if this is enabled and --cors-origins is set to * (default), the Origin header will be echoed back, and credentials will always be allowed<br/>(env: LLAMA_ARG_CORS_CREDENTIALS) |
| `--ui-mcp-proxy, --webui-mcp-proxy, --no-ui-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_UI_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, get_datetime<br/>note: for security reasons, this will limit --cors-origins to localhost by default<br/>(env: LLAMA_ARG_TOOLS) |
| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)<br/>note: for security reasons, this will limit --cors-origins to localhost by default<br/>(env: LLAMA_ARG_AGENT) |
| `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
| `--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, one per line; lines starting with a hash are treated as comments (default: none)<br/>(env: LLAMA_ARG_API_KEY_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_ARG_CHAT_TEMPLATE_KWARGS) |
| `--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) |
| `--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) |
| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |
| `--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) |
| `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
| `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
| `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
| `--spec-draft-cpu-mask, -Cd, --cpu-mask-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
| `--spec-draft-cpu-range, -Crd, --cpu-range-draft lo-hi` | Ranges of CPUs for affinity. Complements --cpu-mask-draft |
| `--spec-draft-cpu-strict, --cpu-strict-draft <0\|1>` | Use strict CPU placement for draft model (default: same as --cpu-strict) |
| `--spec-draft-poll, --poll-draft <0\|1>` | Use polling to wait for draft model work (default: same as --poll) |
| `--spec-draft-cpu-mask-batch, -Cbd, --cpu-mask-batch-draft M` | Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask) |
| `--spec-draft-cpu-strict-batch, --cpu-strict-batch-draft <0\|1>` | Use strict CPU placement for draft model (default: --cpu-strict-draft) |
| `--spec-draft-poll-batch, --poll-batch-draft <0\|1>` | Use polling to wait for draft model work (default: --poll-draft) |
| `--spec-draft-override-tensor, -otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
| `--spec-draft-cpu-moe, -cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_SPEC_DRAFT_CPU_MOE) |
| `--spec-draft-n-cpu-moe, --spec-draft-ncmoe, -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_SPEC_DRAFT_N_CPU_MOE) |
| `--spec-draft-device, -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 |
| `--spec-draft-ngl, -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) |
| `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_MODEL) |
| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,draft-dflash,draft-dspark,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)<br/><br/>(env: LLAMA_ARG_SPEC_TYPE) |
| `--draft, --draft-n, --draft-max N` | the argument has been removed. use --spec-draft-n-max or --spec-ngram-mod-n-max<br/>(env: LLAMA_ARG_DRAFT_MAX) |
| `--draft-min, --draft-n-min N` | the argument has been removed. use --spec-draft-n-min or --spec-ngram-mod-n-min<br/>(env: LLAMA_ARG_DRAFT_MIN) |
| `--spec-ngram-size-n N` | the argument has been removed. use the respective --spec-ngram-*-size-n or --spec-ngram-mod-n-match |
| `--spec-ngram-size-m N` | the argument has been removed. use the respective --spec-ngram-*-size-m |
| `--spec-ngram-min-hits N` | the argument has been removed. use the respective --spec-ngram-*-min-hits |
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.
`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:
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.
`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.
`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`.
`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
`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.
`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`
`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.
`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`
`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.
`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`
`sse_ping_interval`: Interval in seconds between SSE comment pings emitted while the stream stays silent, keeping the connection observable during long prompt processing. Overrides the server `--sse-ping-interval` setting for this request, `-1` disables pings. Default: server setting
`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.
- 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:
- `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.).
- `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`)
`parse_special`: (Optional) Boolean indicating if special tokens should be tokenized. When `false` special tokens are treated as plaintext. Default: `true`
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
### 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`.
This endpoint also supports multimodal embeddings. See the documentation for the `/completions` endpoint for details on how to send a multimodal prompt.
- `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.
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.
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.
In *router mode* the query param `?model={model_id}` has to be set. This endpoint will respond with status code 400 `model name is missing from the request` if not set.
#### Available metrics
| Metric | Type | Description |
| ------ | ---------------------- | ----------- |
| `llamacpp:prompt_tokens_total` | Counter | Number of prompt tokens processed. |
| `llamacpp:prompt_seconds_total` | Counter | Prompt process time in seconds. |
| `llamacpp:prompt_tokens_seconds` | Gauge | Average prompt throughput in tokens/s. |
| `llamacpp:tokens_predicted_total` | Counter | Number of generation tokens processed. |
| `llamacpp:tokens_predicted_seconds_total` | Counter | Predict process time in seconds. |
| `llamacpp:predicted_tokens_seconds` | Gauge | Average generation throughput in tokens/s. |
| `llamacpp:requests_processing` | Gauge | Number of requests processing. |
| `llamacpp:requests_deferred` | Gauge | Number of requests deferred. |
| `llamacpp:n_tokens_max` | Counter | High watermark of the context size observed. |
| `llamacpp:n_decode_total` | Counter | Total Number of llama_decode() calls. |
| `llamacpp:n_busy_slots_per_decode` | Gauge | Average number of busy slots per llama_decode() call. |
`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.
`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.
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`
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`.
### 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.
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.
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.
`reasoning_control`: Arms realtime reasoning control for this completion so it can be ended early via `/v1/chat/completions/control`. Defaults to `false`.
{"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"
[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.
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/chat/completions/control`: Control a running chat completion in real time
Acts on an in-flight completion identified by its `id` (the `id` field streamed back by `/v1/chat/completions`). The request is processed in parallel with the SSE stream, so the client sends it while still reading tokens.
*Options:*
`id`: (Required) The chat completion id to act on. A completion that has already finished matches nothing and the call is a no-op.
`action`: (Required) The control action to perform. Currently the only supported value is `reasoning_end`, which forces the end of the current reasoning block so the model moves on to the final answer. Requires `reasoning_control: true` on the original completion request.
`model`: (Required in router mode) The model name, used to route the request to the right instance. Ignored in single model mode.
**Response format**
Returns a JSON object with a boolean `success` field, and an optional `message` field describing the reason when `success` is `false`.
### POST `/v1/responses/input_tokens`: Token Counting
Similar to [Response input token counts API](https://developers.openai.com/api/reference/python/resources/responses/subresources/input_tokens/methods/count).
Example response:
```json
{
"object": "response.input_tokens",
"input_tokens": 11
}
```
### POST `/v1/chat/completions/input_tokens`: Token Counting
Similar to [Response input token counts API](https://developers.openai.com/api/reference/python/resources/responses/subresources/input_tokens/methods/count), but accepts a chat completion body as input.
Note: This is not an official OAI endpoint, but is added for completeness and convenience.
### 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`)
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)
`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**:
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"
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`.
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:
2. When the model is loaded, the info from `/v1/models` is forwarded to router's `/v1/models`. This includes metadata about the model and the runtime instance.
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.
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:
You can specify default preferences for the web UI using `--ui-config <JSON config>` or `--ui-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:
