fbc98b748e
* Initial XTC commit Adds XTC sampler, not activated by default, but recommended settings by default. * Cleanup * Simplified chances calculation To be more inline with the original implementation, chance is calculated once at the beginning. * First fixes by comments Still need to look into sorting * Fixed trailing backspaces * Fixed RNG to be reproduceable Thanks to @slaren for directions * Fixed forgotten header * Moved `min_keep` Moved from conditions to a simple check at the end. * Fixed broken randomization Thanks to @slaren for explanation * Swapped sorting for a custom algorithm Shifts tokens to remove the penalized ones, then puts the penalized at the back. Should make `min_keep` still viable. * Algorithm rework 1. Scan token from top till the first non-penalizable 2. Remove the last captured token (the least probable above threshold) 3. Shift all tokens to override the remaining penalizable 4. Penalize and put them at the the bottom. * Added XTC to `test-sampling` * Simplified algorithm and more tests * Updated info in common and args * Merged back lost commits in common and arg * Update dump info in common * Fixed incorrect min_keep check * Added XTC to README * Renamed parameters, fixed info and defaults * probability is at 0 by default, but XTC is included in sampling queue * threshold higher than 0.5 switches XTC off * Initial server support * Added XTC to server UIs * Fixed labels in old server UI * Made algorithm safer and more readable * Removed xtc_threshold_max * Fixed arg after update * Quick fixes by comments * Simplified algorithm since threshold_max is removed * Renamed random distribution * Fixed tests and outdated README * Small fixes |
||
---|---|---|
.. | ||
bench | ||
public | ||
public_simplechat | ||
tests | ||
themes | ||
chat-llama2.sh | ||
chat.mjs | ||
chat.sh | ||
CMakeLists.txt | ||
deps.sh | ||
httplib.h | ||
README.md | ||
server.cpp | ||
utils.hpp |
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
- Reranking endoint (WIP: https://github.com/ggerganov/llama.cpp/pull/9510)
- 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
Common params
Argument | Explanation |
---|---|
-h, --help, --usage |
print usage and exit |
--version |
show version and build info |
--verbose-prompt |
print a verbose prompt before generation (default: false) |
-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) |
--prio N |
set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
--poll <0...100> |
use polling level to wait for work (0 - no polling, default: 50) |
-Cb, --cpu-mask-batch M |
CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
-Crb, --cpu-range-batch lo-hi |
ranges of CPUs for affinity. Complements --cpu-mask-batch |
--cpu-strict-batch <0|1> |
use strict CPU placement (default: same as --cpu-strict) |
--prio-batch N |
set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) |
--poll-batch <0|1> |
use polling to wait for work (default: same as --poll) |
-c, --ctx-size N |
size of the prompt context (default: 0, 0 = loaded from model) (env: LLAMA_ARG_CTX_SIZE) |
-n, --predict, --n-predict N |
number of tokens to predict (default: -1, -1 = infinity, -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) |
-fa, --flash-attn |
enable Flash Attention (default: disabled) (env: LLAMA_ARG_FLASH_ATTN) |
-p, --prompt PROMPT |
prompt to start generation with |
--no-perf |
disable internal libllama performance timings (default: false) (env: LLAMA_ARG_NO_PERF) |
-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 |
--rope-scaling {none,linear,yarn} |
RoPE frequency scaling method, defaults to linear unless specified by the model (env: LLAMA_ARG_ROPE_SCALING_TYPE) |
--rope-scale N |
RoPE context scaling factor, expands context by a factor of N (env: LLAMA_ARG_ROPE_SCALE) |
--rope-freq-base N |
RoPE base frequency, used by NTK-aware scaling (default: loaded from model) (env: LLAMA_ARG_ROPE_FREQ_BASE) |
--rope-freq-scale N |
RoPE frequency scaling factor, expands context by a factor of 1/N (env: LLAMA_ARG_ROPE_FREQ_SCALE) |
--yarn-orig-ctx N |
YaRN: original context size of model (default: 0 = model training context size) (env: LLAMA_ARG_YARN_ORIG_CTX) |
--yarn-ext-factor N |
YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation) (env: LLAMA_ARG_YARN_EXT_FACTOR) |
--yarn-attn-factor N |
YaRN: scale sqrt(t) or attention magnitude (default: 1.0) (env: LLAMA_ARG_YARN_ATTN_FACTOR) |
--yarn-beta-slow N |
YaRN: high correction dim or alpha (default: 1.0) (env: LLAMA_ARG_YARN_BETA_SLOW) |
--yarn-beta-fast N |
YaRN: low correction dim or beta (default: 32.0) (env: LLAMA_ARG_YARN_BETA_FAST) |
-dkvc, --dump-kv-cache |
verbose print of the KV cache |
-nkvo, --no-kv-offload |
disable KV offload (env: LLAMA_ARG_NO_KV_OFFLOAD) |
-ctk, --cache-type-k TYPE |
KV cache data type for K (default: f16) (env: LLAMA_ARG_CACHE_TYPE_K) |
-ctv, --cache-type-v TYPE |
KV cache data type for V (default: f16) (env: LLAMA_ARG_CACHE_TYPE_V) |
-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) (env: LLAMA_ARG_N_PARALLEL) |
--mlock |
force system to keep model in RAM rather than swapping or compressing (env: LLAMA_ARG_MLOCK) |
--no-mmap |
do not memory-map model (slower load but may reduce pageouts if not using mlock) (env: LLAMA_ARG_NO_MMAP) |
--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 (env: LLAMA_ARG_NUMA) |
-ngl, --gpu-layers, --n-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 (env: LLAMA_ARG_SPLIT_MODE) |
-ts, --tensor-split N0,N1,N2,... |
fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 (env: LLAMA_ARG_TENSOR_SPLIT) |
-mg, --main-gpu INDEX |
the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) (env: LLAMA_ARG_MAIN_GPU) |
--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 |
-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) |
-ld, --logdir LOGDIR |
path under which to save YAML logs (no logging if unset) |
--log-disable |
Log disable |
--log-file FNAME |
Log to file |
--log-colors |
Enable colored logging (env: LLAMA_LOG_COLORS) |
-v, --verbose, --log-verbose |
Set verbosity level to infinity (i.e. log all messages, useful for debugging) |
-lv, --verbosity, --log-verbosity N |
Set the verbosity threshold. Messages with a higher verbosity will be ignored. (env: LLAMA_LOG_VERBOSITY) |
--log-prefix |
Enable prefx in log messages (env: LLAMA_LOG_PREFIX) |
--log-timestamps |
Enable timestamps in log messages (env: LLAMA_LOG_TIMESTAMPS) |
Sampling params
Argument | Explanation |
---|---|
--samplers SAMPLERS |
samplers that will be used for generation in the order, separated by ';' (default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
-s, --seed SEED |
RNG seed (default: -1, use random seed for -1) |
--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 objectFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
Example-specific params
Argument | Explanation |
---|---|
--no-context-shift |
disables context shift on inifinite text generation (default: disabled) (env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
-sp, --special |
special tokens output enabled (default: false) |
--spm-infill |
use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
--pooling {none,mean,cls,last,rank} |
pooling type for embeddings, use model default if unspecified (env: LLAMA_ARG_POOLING) |
-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) |
-a, --alias STRING |
set alias for model name (to be used by REST API) (env: LLAMA_ARG_ALIAS) |
--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: ) (env: LLAMA_ARG_STATIC_PATH) |
--embedding, --embeddings |
restrict to only support embedding use case; use only with dedicated embedding models (default: disabled) (env: LLAMA_ARG_EMBEDDINGS) |
--reranking, --rerank |
enable reranking endpoint on server (default: disabled) (env: LLAMA_ARG_RERANKING) |
--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 (env: LLAMA_ARG_SSL_KEY_FILE) |
--ssl-cert-file FNAME |
path to file a PEM-encoded SSL certificate (env: LLAMA_ARG_SSL_CERT_FILE) |
-to, --timeout N |
server read/write timeout in seconds (default: 600) (env: LLAMA_ARG_TIMEOUT) |
--threads-http N |
number of threads used to process HTTP requests (default: -1) (env: LLAMA_ARG_THREADS_HTTP) |
--cache-reuse N |
min chunk size to attempt reusing from the cache via KV shifting (default: 0) (env: LLAMA_ARG_CACHE_REUSE) |
--metrics |
enable prometheus compatible metrics endpoint (default: disabled) (env: LLAMA_ARG_ENDPOINT_METRICS) |
--slots |
enable slots monitoring endpoint (default: disabled) (env: LLAMA_ARG_ENDPOINT_SLOTS) |
--props |
enable changing global properties via POST /props (default: disabled) (env: LLAMA_ARG_ENDPOINT_PROPS) |
--no-slots |
disables slots monitoring endpoint (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) |
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.
- Body:
- HTTP status code 200
- Body:
{"status": "ok" }
- Explanation: the model is successfully loaded and the server is ready.
- Body:
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`
`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`
`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.
`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`
`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
), onlycontent
andstop
will be returned until end of completion. -
completion_probabilities
: An array of token probabilities for each completion. The array's length isn_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 (excludingstopping_word
if any). In case of streaming mode, will contain the next token as a string.stop
: Boolean for use withstream
to check whether the generation has stopped (Note: This is not related to stopping words arraystop
from input options)generation_settings
: The provided options above excludingprompt
but includingn_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 providedprompt
stopped_eos
: Indicating whether the completion has stopped because it encountered the EOS tokenstopped_limit
: Indicating whether the completion stopped becausen_predict
tokens were generated before stop words or EOS was encounteredstopped_word
: Indicating whether the completion stopped due to encountering a stopping word fromstop
JSON array providedstopping_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 tokenspredicted_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 prompttruncated
: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (tokens_evaluated
) plus tokens generated (tokens predicted
) exceeded the context size (n_ctx
)
POST /tokenize
: Tokenize a given text
*Options:*
`content`: (Required) The text to tokenize.
`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false`
`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false`
Response:
Returns a JSON object with a tokens
field containing the tokenization result. The tokens
array contains either just token IDs or objects with id
and piece
fields, depending on the with_pieces
parameter. The piece field is a string if the piece is valid unicode or a list of bytes otherwise.
If with_pieces
is false
:
{
"tokens": [123, 456, 789]
}
If with_pieces
is true
:
{
"tokens": [
{"id": 123, "piece": "Hello"},
{"id": 456, "piece": " world"},
{"id": 789, "piece": "!"}
]
}
With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
{
"tokens": [
{"id": 198, "piece": [195]}, // hex C3
{"id": 164, "piece": [161]} // hex A1
]
}
POST /detokenize
: Convert tokens to text
*Options:*
`tokens`: Set the tokens to detokenize.
POST /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 /reranking
: Rerank documents according to a given query
Similar to https://jina.ai/reranker/ but might change in the future.
Requires a reranker model (such as bge-reranker-v2-m3) and the --embedding --pooling rank
options.
*Options:*
`query`: The query against which the documents will be ranked.
`documents`: An array strings representing the documents to be ranked.
*Aliases:*
- `/rerank`
- `/v1/rerank`
- `/v1/reranking`
*Examples:*
```shell
curl http://127.0.0.1:8012/v1/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "some-model",
"query": "What is panda?",
"top_n": 3,
"documents": [
"hi",
"it is a bear",
"The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
]
}' | jq
```
POST /infill
: For code infilling.
Takes a prefix and a suffix and returns the predicted completion as stream.
Options:
input_prefix
: Set the prefix of the code to infill.input_suffix
: Set the suffix of the code to infill.prompt
: Added after theFIM_MID
tokenextra_context
: Additional context inserted before the FIM prefix. See https://github.com/ggerganov/llama.cpp/pull/9874
It also accepts all the options of /completion
.
If the model has FIM_REPO
and FIM_FILE_SEP
tokens, the repo-level pattern is used:
<FIM_REP>myproject
<FIM_SEP>{chunk 0 filename}
{chunk 0 text}
<FIM_SEP>{chunk 1 filename}
{chunk 1 text}
...
<FIM_SEP>filename
<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
If the tokens are missing, then the extra context is simply prefixed at the start:
[extra_context]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
GET /props
: Get server global properties.
This endpoint is public (no API key check). By default, it is read-only. To make POST request to change global properties, you need to start server with --props
Response format
{
"default_generation_settings": { ... },
"total_slots": 1,
"chat_template": ""
}
default_generation_settings
- the default generation settings for the/completion
endpoint, which has the same fields as thegeneration_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 /props
: Change server global properties.
To use this endpoint with POST method, you need to start server with --props
Options:
- None yet
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}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
*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 arraycurl 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_IDLE1
: 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
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>