llama.cpp/examples/server/README.md

# 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 simple web front end 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 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](https://github.com/ggerganov/llama.cpp/issues/4216).

## 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) |
| `-t, --threads N` | number of 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)<br/> |
| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
| `-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)<br/> |
| `--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, -2 = until context filled)<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) |
| `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
| `-p, --prompt PROMPT` | prompt to start generation with |
| `-f, --file FNAME` | a file containing the prompt (default: none) |
| `-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 ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
| `--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.<br/>Top K, Nucleus, Tail Free 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: 0.1) |
| `--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,<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) (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<br/>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)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
| `-np, --parallel N` | number of parallel sequences to decode (default: 1) |
| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
| `-nocb, --no-cont-batching` | disable continuous batching<br/>(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<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/ggerganov/llama.cpp/issues/1437 |
| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<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 |
| `-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.<br/>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<br/>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<br/>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) |
| `-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)<br/>(env: LLAMA_ARG_MODEL) |
| `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
| `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
| `-hff, --hf-file FILE` | Hugging Face model file (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
| `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
| `--port PORT` | port to listen (default: 8080)<br/>(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)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
| `--api-key KEY` | API key to use for authentication (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 |
| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate |
| `-to, --timeout N` | server read/write timeout in seconds (default: 600) |
| `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(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)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
| `--no-slots` | disables slots monitoring endpoint (default: enabled)<br/>(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)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(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)<br/> |
| `--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:

```yml
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`:

  ```bash
  make llama-server
  ```

- 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 `make`:

  ```bash
  # 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`:

  ```bash
  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.)

```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/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](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}'
```

## Advanced testing

We implemented a [server test framework](./tests/README.md) 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](https://nodejs.org/en) installed.

```bash
mkdir llama-client
cd llama-client
```

Create a index.js file and put this inside:

```javascript
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:

```bash
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:

```json
{
  "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](../embedding) 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**

```json
{
  "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](https://github.com/ggerganov/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.

    *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

    ```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"
    }'
    ```

### 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:

```json
[
    {
        "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**

```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`

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

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}
]
```

## 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.

```json
{
    "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](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
```

### 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:

```json
{
    "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**

```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"
    }
}
```

### 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>
```