llama.cpp/examples/server

LLaMA.cpp HTTP Server

Fast, lightweight, pure C/C++ HTTP server based on httplib, nlohmann::json and llama.cpp.

Set of LLM REST APIs and a simple web front end to interact with llama.cpp.

Features:

• LLM inference of F16 and quantized models on GPU and CPU
• OpenAI API compatible chat completions and embeddings routes
• Parallel decoding with multi-user support
• Continuous batching
• Multimodal (wip)
• Monitoring endpoints
• Schema-constrained JSON response format

The project is under active development, and we are looking for feedback and contributors.

Usage

Argument	Explanation
-h, --help, --usage	print usage and exit
--version	show version and build info
-v, --verbose	print verbose information
--verbosity N	set specific verbosity level (default: 0)
--verbose-prompt	print a verbose prompt before generation (default: false)
--no-display-prompt	don't print prompt at generation (default: false)
-s, --seed SEED	RNG seed (default: -1, use random seed for < 0)
-t, --threads N	number of threads to use during generation (default: -1) (env: LLAMA_ARG_THREADS)
-tb, --threads-batch N	number of threads to use during batch and prompt processing (default: same as --threads)
-C, --cpu-mask M	CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "")
-Cr, --cpu-range lo-hi	range of CPUs for affinity. Complements --cpu-mask
--cpu-strict <0|1>	use strict CPU placement (default: 0)
--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)
--poll-batch <0|1>	use polling to wait for work (default: same as --poll)
-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)
-c, --ctx-size N	size of the prompt context (default: 0, 0 = loaded from model) (env: LLAMA_ARG_CTX_SIZE)
-n, --predict, --n-predict N	number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled) (env: LLAMA_ARG_N_PREDICT)
-b, --batch-size N	logical maximum batch size (default: 2048) (env: LLAMA_ARG_BATCH)
-ub, --ubatch-size N	physical maximum batch size (default: 512) (env: LLAMA_ARG_UBATCH)
--keep N	number of tokens to keep from the initial prompt (default: 0, -1 = all)
--chunks N	max number of chunks to process (default: -1, -1 = all)
-fa, --flash-attn	enable Flash Attention (default: disabled) (env: LLAMA_ARG_FLASH_ATTN)
-p, --prompt PROMPT	prompt to start generation with
-f, --file FNAME	a file containing the prompt (default: none)
--in-file FNAME	an input file (repeat to specify multiple files)
-bf, --binary-file FNAME	binary file containing the prompt (default: none)
-e, --escape	process escapes sequences (\n, \r, \t, ', ", \) (default: true)
--no-escape	do not process escape sequences
--spm-infill	use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
--samplers SAMPLERS	samplers that will be used for generation in the order, separated by ';' (default: top_k;tfs_z;typical_p;top_p;min_p;temperature)
--sampling-seq SEQUENCE	simplified sequence for samplers that will be used (default: kfypmt)
--ignore-eos	ignore end of stream token and continue generating (implies --logit-bias EOS-inf)
--penalize-nl	penalize newline tokens (default: false)
--temp N	temperature (default: 0.8)
--top-k N	top-k sampling (default: 40, 0 = disabled)
--top-p N	top-p sampling (default: 0.9, 1.0 = disabled)
--min-p N	min-p sampling (default: 0.1, 0.0 = disabled)
--tfs N	tail free sampling, parameter z (default: 1.0, 1.0 = disabled)
--typical N	locally typical sampling, parameter p (default: 1.0, 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: 1.0, 1.0 = disabled)
--presence-penalty N	repeat alpha presence penalty (default: 0.0, 0.0 = disabled)
--frequency-penalty N	repeat alpha frequency penalty (default: 0.0, 0.0 = disabled)
--dynatemp-range N	dynamic temperature range (default: 0.0, 0.0 = disabled)
--dynatemp-exp N	dynamic temperature exponent (default: 1.0)
--mirostat N	use Mirostat sampling. Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
--mirostat-lr N	Mirostat learning rate, parameter eta (default: 0.1)
--mirostat-ent N	Mirostat target entropy, parameter tau (default: 5.0)
-l, --logit-bias TOKEN_ID(+/-)BIAS	modifies the likelihood of token appearing in the completion, i.e. --logit-bias 15043+1 to increase likelihood of token ' Hello', or --logit-bias 15043-1 to decrease likelihood of token ' Hello'
--grammar GRAMMAR	BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '')
--grammar-file FNAME	file to read grammar from
-j, --json-schema SCHEMA	JSON schema to constrain generations (https://json-schema.org/), e.g. {} for any JSON object For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
--rope-scaling {none,linear,yarn}	RoPE frequency scaling method, defaults to linear unless specified by the model
--rope-scale N	RoPE context scaling factor, expands context by a factor of N
--rope-freq-base N	RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
--rope-freq-scale N	RoPE frequency scaling factor, expands context by a factor of 1/N
--yarn-orig-ctx N	YaRN: original context size of model (default: 0 = model training context size)
--yarn-ext-factor N	YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
--yarn-attn-factor N	YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
--yarn-beta-slow N	YaRN: high correction dim or alpha (default: 1.0)
--yarn-beta-fast N	YaRN: low correction dim or beta (default: 32.0)
-gan, --grp-attn-n N	group-attention factor (default: 1)
-gaw, --grp-attn-w N	group-attention width (default: 512.0)
-dkvc, --dump-kv-cache	verbose print of the KV cache
-nkvo, --no-kv-offload	disable KV offload
-ctk, --cache-type-k TYPE	KV cache data type for K (default: f16)
-ctv, --cache-type-v TYPE	KV cache data type for V (default: f16)
-dt, --defrag-thold N	KV cache defragmentation threshold (default: -1.0, < 0 - disabled) (env: LLAMA_ARG_DEFRAG_THOLD)
-np, --parallel N	number of parallel sequences to decode (default: 1)
-ns, --sequences N	number of sequences to decode (default: 1)
-cb, --cont-batching	enable continuous batching (a.k.a dynamic batching) (default: enabled) (env: LLAMA_ARG_CONT_BATCHING)
-nocb, --no-cont-batching	disable continuous batching (env: LLAMA_ARG_NO_CONT_BATCHING)
--mlock	force system to keep model in RAM rather than swapping or compressing
--no-mmap	do not memory-map model (slower load but may reduce pageouts if not using mlock)
--numa TYPE	attempt optimizations that help on some NUMA systems - distribute: spread execution evenly over all nodes - isolate: only spawn threads on CPUs on the node that execution started on - numactl: use the CPU map provided by numactl if run without this previously, it is recommended to drop the system page cache before using this see https://github.com/ggerganov/llama.cpp/issues/1437
-ngl, --gpu-layers N	number of layers to store in VRAM (env: LLAMA_ARG_N_GPU_LAYERS)
-sm, --split-mode {none,layer,row}	how to split the model across multiple GPUs, one of: - none: use one GPU only - layer (default): split layers and KV across GPUs - row: split rows across GPUs
-ts, --tensor-split N0,N1,N2,...	fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
-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)
--check-tensors	check model tensor data for invalid values (default: false)
--override-kv KEY=TYPE:VALUE	advanced option to override model metadata by key. may be specified multiple times. types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false
--lora FNAME	path to LoRA adapter (can be repeated to use multiple adapters)
--lora-scaled FNAME SCALE	path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)
--control-vector FNAME	add a control vector note: this argument can be repeated to add multiple control vectors
--control-vector-scaled FNAME SCALE	add a control vector with user defined scaling SCALE note: this argument can be repeated to add multiple scaled control vectors
--control-vector-layer-range START END	layer range to apply the control vector(s) to, start and end inclusive
-a, --alias STRING	set alias for model name (to be used by REST API) (env: LLAMA_ARG_MODEL)
-m, --model FNAME	model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise models/7B/ggml-model-f16.gguf) (env: LLAMA_ARG_MODEL)
-mu, --model-url MODEL_URL	model download url (default: unused) (env: LLAMA_ARG_MODEL_URL)
-hfr, --hf-repo REPO	Hugging Face model repository (default: unused) (env: LLAMA_ARG_HF_REPO)
-hff, --hf-file FILE	Hugging Face model file (default: unused) (env: LLAMA_ARG_HF_FILE)
-hft, --hf-token TOKEN	Hugging Face access token (default: value from HF_TOKEN environment variable) (env: HF_TOKEN)
--host HOST	ip address to listen (default: 127.0.0.1) (env: LLAMA_ARG_HOST)
--port PORT	port to listen (default: 8080) (env: LLAMA_ARG_PORT)
--path PATH	path to serve static files from (default: )
--embedding, --embeddings	restrict to only support embedding use case; use only with dedicated embedding models (default: disabled) (env: LLAMA_ARG_EMBEDDINGS)
--api-key KEY	API key to use for authentication (default: none) (env: LLAMA_API_KEY)
--api-key-file FNAME	path to file containing API keys (default: none)
--ssl-key-file FNAME	path to file a PEM-encoded SSL private key
--ssl-cert-file FNAME	path to file a PEM-encoded SSL certificate
--timeout N	server read/write timeout in seconds (default: 600)
--threads-http N	number of threads used to process HTTP requests (default: -1) (env: LLAMA_ARG_THREADS_HTTP)
-spf, --system-prompt-file FNAME	set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications
--log-format {text, json}	log output format: json or text (default: json)
--metrics	enable prometheus compatible metrics endpoint (default: disabled) (env: LLAMA_ARG_ENDPOINT_METRICS)
--no-slots	disables slots monitoring endpoint (default: enabled) (env: LLAMA_ARG_NO_ENDPOINT_SLOTS)
--slot-save-path PATH	path to save slot kv cache (default: disabled)
--chat-template JINJA_TEMPLATE	set custom jinja chat template (default: template taken from model's metadata) if suffix/prefix are specified, template will be disabled only commonly used templates are accepted: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template (env: LLAMA_ARG_CHAT_TEMPLATE)
-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: 0.50, 0.0 = disabled)
--lora-init-without-apply	load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled)
-ld, --logdir LOGDIR	path under which to save YAML logs (no logging if unset)
--log-test	Log test
--log-disable	Log disable
--log-enable	Log enable
--log-new	Log new
--log-append	Log append
--log-file FNAME	Log file

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

Example usage of docker compose with environment variables:

services:
  llamacpp-server:
    image: ghcr.io/ggerganov/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

Build

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

• Using make:

  make llama-server
  

• Using CMake:

  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 make:

  # NOTE: For non-system openssl, use the following:
  #   CXXFLAGS="-I /path/to/openssl/include"
  #   LDFLAGS="-L /path/to/openssl/lib"
  make LLAMA_SERVER_SSL=true llama-server
  

• Using CMake:

  cmake -B build -DLLAMA_SERVER_SSL=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.)

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

Windows

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

docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/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/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99

Testing with CURL

Using curl. On Windows, curl.exe should be available in the base OS.

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}'

Advanced testing

We implemented a server test framework using human-readable scenario.

Before submitting an issue, please try to reproduce it with this format.

Node JS Test

You need to have Node.js installed.

mkdir llama-client
cd llama-client

Create a index.js file and put this inside:

const prompt = `Building a website can be done in 10 simple steps:`;

async function Test() {
    let response = await fetch("http://127.0.0.1:8080/completion", {
        method: 'POST',
        body: JSON.stringify({
            prompt,
            n_predict: 512,
        })
    })
    console.log((await response.json()).content)
}

Test()

And run it:

node index.js

API Endpoints

GET /health: Returns heath check result

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.

*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`
  - The system prompt is empty

`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_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.

`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. 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: `[]`

`tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.

`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.

`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`

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

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

`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. 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`

`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.

`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: `false`

`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)

`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: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.

Response format

• Note: When using streaming mode (stream), only content and stop will be returned until end of completion.

• completion_probabilities: An array of token probabilities for each completion. The array's length is n_predict. Each item in the array has the following structure:

{
  "content": "<the token selected by the model>",
  "probs": [
    {
      "prob": float,
      "tok_str": "<most likely token>"
    },
    {
      "prob": float,
      "tok_str": "<second most likely token>"
    },
    ...
  ]
},

Notice that each probs is an array of length 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.
• 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 path to the model loaded with -m
• prompt: The provided prompt
• stopped_eos: Indicating whether the completion has stopped because it encountered the EOS token
• stopped_limit: Indicating whether the completion stopped because n_predict tokens were generated before stop words or EOS was encountered
• stopped_word: Indicating whether the completion 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 (n_past)
• 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`: Set the text to tokenize.

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

POST /detokenize: Convert tokens to text

*Options:*

`tokens`: Set the tokens to detokenize.

POST /embedding: Generate embedding of a given text

The same as the embedding example does.

*Options:*

`content`: Set the text to process.

`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.

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.

It also accepts all the options of `/completion` except `stream` and `prompt`.

• GET /props: Return current server settings.

Response format

{
  "assistant_name": "",
  "user_name": "",
  "default_generation_settings": { ... },
  "total_slots": 1,
  "chat_template": ""
}

• assistant_name - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
• user_name - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
• 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)
• chat_template - the model's original Jinja2 prompt template

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 can be used optimally with this endpoint. By default, the ChatML template will be used.

*Options:*

See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are 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}}`), similar to other OpenAI-inspired API providers.

*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"
}
]
}'
```

POST /v1/embeddings: OpenAI-compatible embeddings API

*Options:*

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

*Examples:*

• input as string

  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

  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"
  }'
  

GET /slots: Returns the current slots processing state

This endpoint can be disabled with --no-slots

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

Example:

[
    {
        "dynatemp_exponent": 1.0,
        "dynatemp_range": 0.0,
        "frequency_penalty": 0.0,
        "grammar": "",
        "id": 0,
        "ignore_eos": false,
        "logit_bias": [],
        "min_p": 0.05000000074505806,
        "mirostat": 0,
        "mirostat_eta": 0.10000000149011612,
        "mirostat_tau": 5.0,
        "model": "llama-2-7b-32k-instruct.Q2_K.gguf",
        "n_ctx": 2048,
        "n_keep": 0,
        "n_predict": 100000,
        "n_probs": 0,
        "next_token": {
            "has_next_token": true,
            "n_remain": -1,
            "n_decoded": 0,
            "stopped_eos": false,
            "stopped_limit": false,
            "stopped_word": false,
            "stopping_word": ""
        },
        "penalize_nl": true,
        "presence_penalty": 0.0,
        "prompt": "Say hello to llama.cpp",
        "repeat_last_n": 64,
        "repeat_penalty": 1.100000023841858,
        "samplers": [
            "top_k",
            "tfs_z",
            "typical_p",
            "top_p",
            "min_p",
            "temperature"
        ],
        "seed": 42,
        "state": 1,
        "stop": [
            "\n"
        ],
        "stream": false,
        "task_id": 0,
        "temperature": 0.0,
        "tfs_z": 1.0,
        "top_k": 40,
        "top_p": 0.949999988079071,
        "typical_p": 1.0
    }
]

Possible values for slot[i].state are:

• 0: SLOT_STATE_IDLE
• 1: SLOT_STATE_PROCESSING

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.

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

{
    "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

{
    "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

{
    "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

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

Response format

[
    {
        "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

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

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

More examples

Change system prompt on runtime

To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option system_prompt. This only needs to be used once.

prompt: Specify a context that you want all connecting clients to respect.

anti_prompt: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the /props endpoint.

assistant_name: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the /props endpoint.

{
    "system_prompt": {
        "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:",
        "anti_prompt": "User:",
        "assistant_name": "Assistant:"
    }
}

NOTE: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option -spf FNAME or --system-prompt-file FNAME.

Interactive mode

Check the sample in chat.mjs. Run with NodeJS version 16 or later:

node chat.mjs

Another sample in chat.sh. Requires bash, curl and jq. Run with bash:

bash chat.sh

OAI-like API

The HTTP llama-server supports an OAI-like API: https://github.com/openai/openai-openapi

API errors

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

Example of an error:

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

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

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

*When the server receives invalid grammar via /completions endpoint

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

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>
  <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>