OpenAI-Compatible API Server
This page assumes you've already installed Aphrodite and know how to launch the OpenAI-Compatible server.
INFO
This page is quite large and extensive; please use the table of contents ("On this page" to the top left) to navigate.
API Reference
Please see the OpenAI API Reference for more information on the API scheme, as we support all parameters, except:
- in
/v1/chat/completions:toolsandtool_choice - in
/v1/completions:suffix
Otherwise, we support everything, plus many other parameters.
Aphrodite also provides experimental support for the OpenAI Vision API.
Extra Parameters
If using the openai python library, you cannot pass extra parameters such as min_p, guided_choice, etc. Thankfully, the library allows you to extend the body as needed:
py
completion = client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
messages=[
{"role": "user", "content": "Classify this sentiment: LLMs are wonderful!"}
],
extra_body={
"guided_choice": ["positive", "negative"]
}
)Extra Parameters for Chat API
Aphrodite supports the following extra parameters that are not supported by OpenAI:
py
best_of: Optional[int] = None
use_beam_search: Optional[bool] = False
top_k: Optional[int] = -1
min_p: Optional[float] = 0.0
top_a: Optional[float] = 0.0
tfs: Optional[float] = 1.0
eta_cutoff: Optional[float] = 0.0
epsilon_cutoff: Optional[float] = 0.0
typical_p: Optional[float] = 1.0
smoothing_factor: Optional[float] = 0.0
smoothing_curve: Optional[float] = 1.0
repetition_penalty: Optional[float] = 1.0
length_penalty: Optional[float] = 1.0
early_stopping: Optional[bool] = False
ignore_eos: Optional[bool] = False
min_tokens: Optional[int] = 0
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = NoneAnd the following parameters:
py
echo: Optional[bool] = Field(
default=False,
description=(
"If true, the new message will be prepended with the last message "
"if they belong to the same role."),
)
add_generation_prompt: Optional[bool] = Field(
default=True,
description=
("If true, the generation prompt will be added to the chat template. "
"This is a parameter used by chat template in tokenizer config of the "
"model."),
)
add_special_tokens: Optional[bool] = Field(
default=False,
description=(
"If true, special tokens (e.g. BOS) will be added to the prompt "
"on top of what is added by the chat template. "
"For most models, the chat template takes care of adding the "
"special tokens so this should be set to False (as is the "
"default)."),
)
documents: Optional[List[Dict[str, str]]] = Field(
default=None,
description=
("A list of dicts representing documents that will be accessible to "
"the model if it is performing RAG (retrieval-augmented generation)."
" If the template does not support RAG, this argument will have no "
"effect. We recommend that each document should be a dict containing "
"\"title\" and \"text\" keys."),
)
chat_template: Optional[str] = Field(
default=None,
description=(
"A Jinja template to use for this conversion. "
"If this is not passed, the model's default chat template will be "
"used instead."),
)
chat_template_kwargs: Optional[Dict[str, Any]] = Field(
default=None,
description=("Additional kwargs to pass to the template renderer. "
"Will be accessible by the chat template."),
)
include_stop_str_in_output: Optional[bool] = Field(
default=False,
description=(
"Whether to include the stop string in the output. "
"This is only applied when the stop or stop_token_ids is set."),
)
guided_json: Optional[Union[str, dict, BaseModel]] = Field(
default=None,
description=("If specified, the output will follow the JSON schema."),
)
guided_regex: Optional[str] = Field(
default=None,
description=(
"If specified, the output will follow the regex pattern."),
)
guided_choice: Optional[List[str]] = Field(
default=None,
description=(
"If specified, the output will be exactly one of the choices."),
)
guided_grammar: Optional[str] = Field(
default=None,
description=(
"If specified, the output will follow the context free grammar."),
)
guided_decoding_backend: Optional[str] = Field(
default=None,
description=(
"If specified, will override the default guided decoding backend "
"of the server for this specific request. If set, must be either "
"'outlines' / 'lm-format-enforcer'"))
guided_whitespace_pattern: Optional[str] = Field(
default=None,
description=(
"If specified, will override the default whitespace pattern "
"for guided json decoding."))Extra Parameters for Text Completions API
Aphrodite supports the following extra parameters that are not supported by OpenAI:
py
use_beam_search: Optional[bool] = False
top_k: Optional[int] = -1
min_p: Optional[float] = 0.0
top_a: Optional[float] = 0.0
tfs: Optional[float] = 1.0
eta_cutoff: Optional[float] = 0.0
epsilon_cutoff: Optional[float] = 0.0
typical_p: Optional[float] = 1.0
smoothing_factor: Optional[float] = 0.0
smoothing_curve: Optional[float] = 1.0
repetition_penalty: Optional[float] = 1.0
length_penalty: Optional[float] = 1.0
early_stopping: Optional[bool] = False
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
ignore_eos: Optional[bool] = False
min_tokens: Optional[int] = 0
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
allowed_token_ids: Optional[List[int]] = None
include_stop_str_in_output: Optional[bool] = False
add_special_tokens: Optional[bool] = FalseAnd the following parameters:
py
response_format: Optional[ResponseFormat] = Field(
default=None,
description=
("Similar to chat completion, this parameter specifies the format of "
"output. Only {'type': 'json_object'} or {'type': 'text' } is "
"supported."),
)
guided_json: Optional[Union[str, dict, BaseModel]] = Field(
default=None,
description=("If specified, the output will follow the JSON schema."),
)
guided_regex: Optional[str] = Field(
default=None,
description=(
"If specified, the output will follow the regex pattern."),
)
guided_choice: Optional[List[str]] = Field(
default=None,
description=(
"If specified, the output will be exactly one of the choices."),
)
guided_grammar: Optional[str] = Field(
default=None,
description=(
"If specified, the output will follow the context free grammar."),
)
guided_decoding_backend: Optional[str] = Field(
default=None,
description=(
"If specified, will override the default guided decoding backend "
"of the server for this specific request. If set, must be one of "
"'outlines' / 'lm-format-enforcer'"))
guided_whitespace_pattern: Optional[str] = Field(
default=None,
description=(
"If specified, will override the default whitespace pattern "
"for guided json decoding."))Chat Template
In order for the LLM to support chat completions protocol, Aphrodite requires the model to include a chat template in its tokenizer config. The chat template is Jinja2 template file that specifies how roles, messages, and other chat-specific tokens are encoded in the input.
Most modern LLMs provide this if they're an Instruct/Chat finetune, but sometimes they may not. For those models, you can manually specify their chat template in the --chat-template (or - chat_template in the YAML) with the path being the URL or local disk path. You may also provide it as in-line string to the argument. Without a chat template, the server will only launch text completions.
Aphrodite provides a set of chat templates, which you can view here.
Command-line arguments for the server
console
usage: aphrodite run <model_tag> [options]
positional arguments:
model_tag The model tag to serve
options:
-h, --help show this help message and exit
--host HOST host name
--port PORT port number
--uvicorn-log-level {debug,info,warning,error,critical,trace}
log level for uvicorn
--allow-credentials allow credentials
--allowed-origins ALLOWED_ORIGINS
allowed origins
--allowed-methods ALLOWED_METHODS
allowed methods
--allowed-headers ALLOWED_HEADERS
allowed headers
--api-keys API_KEYS If provided, the server will require this key to be
presented in the header.
--admin-key ADMIN_KEY
If provided, the server will require this key to be
presented in the header for admin operations.
--lora-modules LORA_MODULES [LORA_MODULES ...]
LoRA module configurations in the format name=path.
Multiple modules can be specified.
--prompt-adapters PROMPT_ADAPTERS [PROMPT_ADAPTERS ...]
Prompt adapter configurations in the format name=path.
Multiple adapters can be specified.
--chat-template CHAT_TEMPLATE
The file path to the chat template, or the template in
single-line form for the specified model
--response-role RESPONSE_ROLE
The role name to return if
`request.add_generation_prompt=true`.
--ssl-keyfile SSL_KEYFILE
The file path to the SSL key file
--ssl-certfile SSL_CERTFILE
The file path to the SSL cert file
--ssl-ca-certs SSL_CA_CERTS
The CA certificates file
--ssl-cert-reqs SSL_CERT_REQS
Whether client certificate is required (see stdlib ssl
module's)
--root-path ROOT_PATH
FastAPI root_path when app is behind a path based
routing proxy
--middleware MIDDLEWARE
Additional ASGI middleware to apply to the app. We
accept multiple --middleware arguments. The value
should be an import path. If a function is provided,
Aphrodite will add it to the server using
@app.middleware('http'). If a class is provided,
Aphrodite will add it to the server using
app.add_middleware().
--launch-kobold-api Launch the Kobold API server alongside the OpenAI
server
--max-log-len MAX_LOG_LEN
Max number of prompt characters or prompt ID numbers
being printed in log. Default: 0
--return-tokens-as-token-ids
When --max-logprobs is specified, represents single
tokens asstrings of the form 'token_id:{token_id}' so
that tokens thatare not JSON-encodable can be
identified.
--disable-frontend-multiprocessing
If specified, will run the OpenAI frontend server in
the same process as the model serving engine.
--model MODEL Category: Model Options name or path of the
huggingface model to use
--seed SEED Category: Model Options random seed
--served-model-name SERVED_MODEL_NAME [SERVED_MODEL_NAME ...]
Category: API Options The model name(s) used in the
API. If multiple names are provided, the server will
respond to any of the provided names. The model name
in the model field of a response will be the first
name in this list. If not specified, the model name
will be the same as the `--model` argument. Noted that
this name(s)will also be used in `model_name` tag
content of prometheus metrics, if multiple names
provided, metricstag will take the first one.
--tokenizer TOKENIZER
Category: Model Options name or path of the
huggingface tokenizer to use
--revision REVISION Category: Model Options the specific model version to
use. It can be a branch name, a tag name, or a commit
id. If unspecified, will use the default version.
--code-revision CODE_REVISION
Category: Model Options the specific revision to use
for the model code on Hugging Face Hub. It can be a
branch name, a tag name, or a commit id. If
unspecified, will use the default version.
--tokenizer-revision TOKENIZER_REVISION
Category: Model Options the specific tokenizer version
to use. It can be a branch name, a tag name, or a
commit id. If unspecified, will use the default
version.
--tokenizer-mode {auto,slow}
Category: Model Options tokenizer mode. "auto" will
use the fast tokenizer if available, and "slow" will
always use the slow tokenizer.
--trust-remote-code Category: Model Options trust remote code from
huggingface
--download-dir DOWNLOAD_DIR
Category: Model Options directory to download and load
the weights, default to the default cache dir of
huggingface
--max-model-len MAX_MODEL_LEN
Category: Model Options model context length. If
unspecified, will be automatically derived from the
model.
--max-context-len-to-capture MAX_CONTEXT_LEN_TO_CAPTURE
Category: Model Options Maximum context length covered
by CUDA graphs. When a sequence has context length
larger than this, we fall back to eager mode.
(DEPRECATED. Use --max-seq_len-to-capture instead)
--max-seq_len-to-capture MAX_SEQ_LEN_TO_CAPTURE
Category: Model Options Maximum sequence length
covered by CUDA graphs. When a sequence has context
length larger than this, we fall back to eager mode.
--rope-scaling ROPE_SCALING
Category: Model Options RoPE scaling configuration in
JSON format. For example,
{"type":"dynamic","factor":2.0}
--rope-theta ROPE_THETA
Category: Model Options RoPE theta. Use with
`rope_scaling`. In some cases, changing the RoPE theta
improves the performance of the scaled model.
--model-loader-extra-config MODEL_LOADER_EXTRA_CONFIG
Category: Model Options Extra config for model loader.
This will be passed to the model loader corresponding
to the chosen load_format. This should be a JSON
string that will be parsed into a dictionary.
--enforce-eager [ENFORCE_EAGER]
Category: Model Options Always use eager-mode PyTorch.
If False, will use eager mode and CUDA graph in hybrid
for maximal performance and flexibility.
--skip-tokenizer-init
Category: Model Options Skip initialization of
tokenizer and detokenizer
--tokenizer-pool-size TOKENIZER_POOL_SIZE
Category: Model Options Size of tokenizer pool to use
for asynchronous tokenization. If 0, will use
synchronous tokenization.
--tokenizer-pool-type TOKENIZER_POOL_TYPE
Category: Model Options The type of tokenizer pool to
use for asynchronous tokenization. Ignored if
tokenizer_pool_size is 0.
--tokenizer-pool-extra-config TOKENIZER_POOL_EXTRA_CONFIG
Category: Model Options Extra config for tokenizer
pool. This should be a JSON string that will be parsed
into a dictionary. Ignored if tokenizer_pool_size is
0.
--max-logprobs MAX_LOGPROBS
Category: Model Options maximum number of log
probabilities to return.
--device {auto,cuda,neuron,cpu,openvino,tpu,xpu}
Category: Model Options Device to use for model
execution.
--load-format {auto,pt,safetensors,npcache,dummy,tensorizer,sharded_state,bitsandbytes}
Category: Model Options The format of the model
weights to load. * "auto" will try to load the weights
in the safetensors format and fall back to the pytorch
bin format if safetensors format is not available. *
"pt" will load the weights in the pytorch bin format.
* "safetensors" will load the weights in the
safetensors format. * "npcache" will load the weights
in pytorch format and store a numpy cache to speed up
the loading. * "dummy" will initialize the weights
with random values, which is mainly for profiling. *
"tensorizer" will load the weights using tensorizer
from CoreWeave. See the Tensorize Aphrodite Model
script in the Examples section for more information. *
"bitsandbytes" will load the weights using
bitsandbytes quantization.
--dtype {auto,half,float16,bfloat16,float,float32}
Category: Model Options Data type for model weights
and activations. * "auto" will use FP16 precision for
FP32 and FP16 models, and BF16 precision for BF16
models. * "half" for FP16. Recommended for AWQ
quantization. * "float16" is the same as "half". *
"bfloat16" for a balance between precision and range.
* "float" is shorthand for FP32 precision. * "float32"
for FP32 precision.
--ignore-patterns IGNORE_PATTERNS
Category: Model Options The pattern(s) to ignore when
loading the model.Defaults to 'original/**/*' to avoid
repeated loading of llama's checkpoints.
--worker-use-ray Category: Parallel Options Deprecated, use
--distributed-executor-backend=ray.
--tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
Category: Parallel Options number of tensor parallel
replicas, i.e. the number of GPUs to use.
--pipeline-parallel-size PIPELINE_PARALLEL_SIZE, -pp PIPELINE_PARALLEL_SIZE
Category: Parallel Options number of pipeline stages.
Currently not supported.
--ray-workers-use-nsight
Category: Parallel Options If specified, use nsight to
profile ray workers
--disable-custom-all-reduce
Category: Model Options See ParallelConfig
--distributed-executor-backend {ray,mp}
Category: Parallel Options Backend to use for
distributed serving. When more than 1 GPU is used,
will be automatically set to "ray" if installed or
"mp" (multiprocessing) otherwise.
--max-parallel-loading-workers MAX_PARALLEL_LOADING_WORKERS
Category: Parallel Options load model sequentially in
multiple batches, to avoid RAM OOM when using tensor
parallel and large models
--quantization {aqlm,awq,deepspeedfp,eetq,fp8,fbgemm_fp8,gguf,marlin,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,quip,squeezellm,compressed-tensors,bitsandbytes,qqq,None}, -q {aqlm,awq,deepspeedfp,eetq,fp8,fbgemm_fp8,gguf,marlin,gptq_marlin_24,gptq_marlin,awq_marlin,gptq,quip,squeezellm,compressed-tensors,bitsandbytes,qqq,None}
Category: Quantization Options Method used to quantize
the weights. If None, we first check the
`quantization_config` attribute in the model config
file. If that is None, we assume the model weights are
not quantized and use `dtype` to determine the data
type of the weights.
--quantization-param-path QUANTIZATION_PARAM_PATH
Category: Quantization Options Path to the JSON file
containing the KV cache scaling factors. This should
generally be supplied, when KV cache dtype is FP8.
Otherwise, KV cache scaling factors default to 1.0,
which may cause accuracy issues. FP8_E5M2 (without
scaling) is only supported on cuda versiongreater than
11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported
for common inference criteria.
--preemption-mode PREEMPTION_MODE
Category: Scheduler Options If 'recompute', the engine
performs preemption by block swapping; If 'swap', the
engine performs preemption by block swapping.
--deepspeed-fp-bits DEEPSPEED_FP_BITS
Category: Quantization Options Number of floating bits
to use for the deepseed quantization. Supported bits
are: 4, 6, 8, 12.
--kv-cache-dtype {auto,fp8,fp8_e5m2,fp8_e4m3}
Category: Cache Options Data type for kv cache
storage. If "auto", will use model data type. CUDA
11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD
GPU) supports fp8 (=fp8_e4m3)
--block-size {8,16,32}
Category: Cache Options token block size
--enable-prefix-caching, --context-shift
Category: Cache Options Enable automatic prefix
caching.
--num-gpu-blocks-override NUM_GPU_BLOCKS_OVERRIDE
Category: Cache Options Options If specified, ignore
GPU profiling result and use this number of GPU
blocks. Used for testing preemption.
--disable-sliding-window
Category: KV Cache Options Disables sliding window,
capping to sliding window size
--gpu-memory-utilization GPU_MEMORY_UTILIZATION, -gmu GPU_MEMORY_UTILIZATION
Category: Cache Options The fraction of GPU memory to
be used for the model executor, which can range from 0
to 1.If unspecified, will use the default value of
0.9.
--swap-space SWAP_SPACE
Category: Cache Options CPU swap space size (GiB) per
GPU
--cpu-offload-gb CPU_OFFLOAD_GB
Category: Cache Options The space in GiB to offload to
CPU, per GPU. Default is 0, which means no offloading.
Intuitively, this argument can be seen as a virtual
way to increase the GPU memory size. For example, if
you have one 24 GB GPU and set this to 10, virtually
you can think of it as a 34 GB GPU. Then you can load
a 13B model with BF16 weight,which requires at least
26GB GPU memory. Note that this requires fast CPU-GPU
interconnect, as part of the model isloaded from CPU
memory to GPU memory on the fly in each model forward
pass.
--use-v2-block-manager
Category: Scheduler Options Use the v2 block manager.
--scheduler-delay-factor SCHEDULER_DELAY_FACTOR, -sdf SCHEDULER_DELAY_FACTOR
Category: Scheduler Options Apply a delay (of delay
factor multiplied by previous prompt latency) before
scheduling next prompt.
--enable-chunked-prefill [ENABLE_CHUNKED_PREFILL]
Category: Scheduler Options If True, the prefill
requests can be chunked based on the
max_num_batched_tokens.
--guided-decoding-backend {outlines,lm-format-enforcer}
Category: Scheduler Options Which engine will be used
for guided decoding (JSON schema / regex etc) by
default. Currently support
https://github.com/outlines-dev/outlines and
https://github.com/noamgat/lm-format-enforcer. Can be
overridden per request via guided_decoding_backend
parameter.
--max-num-batched-tokens MAX_NUM_BATCHED_TOKENS
Category: KV Cache Options maximum number of batched
tokens per iteration
--max-num-seqs MAX_NUM_SEQS
Category: API Options maximum number of sequences per
iteration
--num-lookahead-slots NUM_LOOKAHEAD_SLOTS
Category: Speculative Decoding Options Experimental
scheduling config necessary for speculative decoding.
This will be replaced by speculative decoding config
in the future; it is present for testing purposes
until then.
--speculative-model SPECULATIVE_MODEL
Category: Speculative Decoding Options The name of the
draft model to be used in speculative decoding.
--num-speculative-tokens NUM_SPECULATIVE_TOKENS
Category: Speculative Decoding Options The number of
speculative tokens to sample from the draft model in
speculative decoding
--speculative-max-model-len SPECULATIVE_MAX_MODEL_LEN
Category: Speculative Decoding Options The maximum
sequence length supported by the draft model.
Sequences over this length will skip speculation.
--ngram-prompt-lookup-max NGRAM_PROMPT_LOOKUP_MAX
Category: Speculative Decoding Options Max size of
window for ngram prompt lookup in speculative
decoding.
--ngram-prompt-lookup-min NGRAM_PROMPT_LOOKUP_MIN
Category: Speculative Decoding Options Min size of
window for ngram prompt lookup in speculative
decoding.
--speculative-draft-tensor-parallel-size SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE, -spec-draft-tp SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE
Category: Speculative Decoding Options Number of
tensor parallel replicas for the draft model in
speculative decoding.
--speculative-disable-by-batch-size SPECULATIVE_DISABLE_BY_BATCH_SIZE
Category: Speculative Decoding Options Disable
speculative decoding for new incoming requests if the
number of enqueue requests is larger than this value.
--spec-decoding-acceptance-method {rejection_sampler,typical_acceptance_sampler}
Category: Speculative Decoding Options Specify the
acceptance method to use during draft token
verification in speculative decoding. Two types of
acceptance routines are supported: 1) RejectionSampler
which does not allow changing the acceptance rate of
draft tokens, 2) TypicalAcceptanceSampler which is
configurable, allowing for a higher acceptance rate at
the cost of lower quality, and vice versa.
--typical-acceptance-sampler-posterior-threshold TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD
Category: Speculative Decoding Options Set the lower
bound threshold for the posterior probability of a
token to be accepted. This threshold is used by the
TypicalAcceptanceSampler to make sampling decisions
during speculative decoding. Defaults to 0.09
--typical-acceptance-sampler-posterior-alpha TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA
Category: Speculative Decoding Options A scaling
factor for the entropy-based threshold for token
acceptance in the TypicalAcceptanceSampler. Typically
defaults to sqrt of --typical-acceptance-sampler-
posterior-threshold i.e. 0.3
--disable-logprobs-during-spec-decoding DISABLE_LOGPROBS_DURING_SPEC_DECODING
Category: Speculative Decoding Options If set to True,
token log probabilities are not returned during
speculative decoding. If set to False, log
probabilities are returned according to the settings
in SamplingParams. If not specified, it defaults to
True. Disabling log probabilities during speculative
decoding reduces latency by skipping logprob
calculation in proposal sampling, target sampling, and
after accepted tokens are determined.
--enable-lora Category: Adapter Options If True, enable handling of
LoRA adapters.
--max-loras MAX_LORAS
Category: Adapter Options Max number of LoRAs in a
single batch.
--max-lora-rank MAX_LORA_RANK
Category: Adapter Options Max LoRA rank.
--lora-extra-vocab-size LORA_EXTRA_VOCAB_SIZE
Category: Adapter Options Maximum size of extra
vocabulary that can be present in a LoRA adapter
(added to the base model vocabulary).
--lora-dtype {auto,float16,bfloat16,float32}
Category: Adapter Options Data type for LoRA. If auto,
will default to base model dtype.
--max-cpu-loras MAX_CPU_LORAS
Category: Adapter Options Maximum number of LoRAs to
store in CPU memory. Must be >= than max_num_seqs.
Defaults to max_num_seqs.
--long-lora-scaling-factors LONG_LORA_SCALING_FACTORS
Category: Adapter Options Specify multiple scaling
factors (which can be different from base model
scaling factor - see eg. Long LoRA) to allow for
multiple LoRA adapters trained with those scaling
factors to be used at the same time. If not specified,
only adapters trained with the base model scaling
factor are allowed.
--fully-sharded-loras
Category: Adapter Options By default, only half of the
LoRA computation is sharded with tensor parallelism.
Enabling this will use the fully sharded layers. At
high sequence length, max rank or tensor parallel
size, this is likely faster.
--qlora-adapter-name-or-path QLORA_ADAPTER_NAME_OR_PATH
Category: Adapter Options Name or path of the LoRA
adapter to use.
--enable-prompt-adapter
Category: Adapter Options If True, enable handling of
PromptAdapters.
--max-prompt-adapters MAX_PROMPT_ADAPTERS
Category: Adapter Options Max number of PromptAdapters
in a batch.
--max-prompt-adapter-token MAX_PROMPT_ADAPTER_TOKEN
Category: Adapter Options Max number of PromptAdapters
tokens
--disable-log-stats Category: Log Options disable logging statistics
--engine-use-ray Use Ray to start the LLM engine in a separate process
as the server process.
--disable-log-requests
Disable logging requests.
--uvloop Use the Uvloop asyncio event loop to possibly increase
performanceTool Calling in the chat completions API
Aphrodite supports only named function calling in the chat completions API. The tool_choice options auto and required are not yet supported but on the development roadmap.
To use a named function, you need to define the function in the tools parameter and call it in the tools_choice parameter. It's the caller's responsibility to prompt the model with the tool info; Aphrodite will not automatically manipulate the prompt. This may change in the future!
Aphrodite will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the tools parameter. Please refer to the OpenAI API reference for more info.