import { Secret } from "aws-cdk-lib/aws-batch";
/**
 * Log level options for Uvicorn
 */
export declare enum UvicornLogLevel {
    DEBUG = "debug",
    INFO = "info",
    WARNING = "warning",
    ERROR = "error",
    CRITICAL = "critical",
    TRACE = "trace"
}
/**
 * Available model weight loading formats
 */
export declare enum LoadFormat {
    AUTO = "auto",
    PT = "pt",
    SAFETENSORS = "safetensors",
    NPCACHE = "npcache",
    DUMMY = "dummy",
    TENSORIZER = "tensorizer",
    SHARDED_STATE = "sharded_state",
    GGUF = "gguf",
    BITSANDBYTES = "bitsandbytes",
    MISTRAL = "mistral",
    RUNAI_STREAMER = "runai_streamer",
    FASTSAFETENSORS = "fastsafetensors"
}
/**
 * Data types for model weights and activations
 */
export declare enum DataType {
    /**
     * “auto” will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models.
     */
    AUTO = "auto",
    /**
     * “half” for FP16. Recommended for AWQ quantization.
     */
    HALF = "half",
    /**
     * “float16” is the same as “half”.
     */
    FLOAT16 = "float16",
    /**
     * “bfloat16” for a balance between precision and range.
     */
    BFLOAT16 = "bfloat16",
    /**
     * “float” is shorthand for FP32 precision.
     */
    FLOAT = "float",
    /**
     * “float32” for FP32 precision.
     */
    FLOAT32 = "float32"
}
/**
 * The folder path to the generation config.
 */
export declare enum GenerationConfig {
    AUTO = "auto",
    VLLM = "vllm"
}
/**
 * Available guided decoding backends
 */
export declare enum GuidedDecodingBackend {
    AUTO = "auto",
    OUTLINES = "outlines",
    LM_FORMAT_ENFORCER = "lm-format-enforcer",
    XGRAMMAR = "xgrammar"
}
/**
 * Available reasoning parsers
 */
export declare enum ReasoningParser {
    DEEPSEEK_R1 = "deepseek_r1",
    GRANITE = "granite"
}
/**
 * Model implementation options
 */
export declare enum ModelImpl {
    /**
     * “auto” will try to use the vLLM implementation if it exists and fall back to the Transformers
     * implementation if no vLLM implementation is available.
     */
    AUTO = "auto",
    /**
     * “vllm” will use the vLLM model implementation.
     */
    VLLM = "vllm",
    /**
     * “transformers” will use the Transformers model implementation.
     */
    TRANSFORMERS = "transformers"
}
/**
 * Distributed execution backend options
 */
export declare enum DistributedExecutorBackend {
    RAY = "ray",
    MP = "mp",
    UNI = "uni",
    EXTERNAL_LAUNCHER = "external_launcher"
}
/**
 * Cache block size options in number of tokens
 */
export declare enum BlockSize {
    SIZE_8 = 8,
    SIZE_16 = 16,
    SIZE_32 = 32,
    SIZE_64 = 64,
    SIZE_128 = 128
}
/**
 * KV cache data type options
 */
export declare enum KvCacheDtype {
    AUTO = "auto",
    FP8 = "fp8",
    FP8_E4M3 = "fp8_e4m3",
    FP8_E5M2 = "fp8_e5m2"
}
/**
 * Hash algorithm options for prefix caching
 */
export declare enum PrefixCachingHashAlgo {
    /**
     * “builtin” is Python’s built-in hash.
     */
    BUILTIN = "builtin",
    /**
     * “sha256” is collision resistant but with certain overheads.
     */
    SHA256 = "sha256"
}
/**
 * Quantization methods
 */
export declare enum Quantization {
    AQLM = "aqlm",
    AWQ = "awq",
    DEEPSPEEDFP = "deepspeedfp",
    TPU_INT8 = "tpu_int8",
    FP8 = "fp8",
    PTPC_FP8 = "ptpc_fp8",
    FBGEMM_FP8 = "fbgemm_fp8",
    MODELOPT = "modelopt",
    NVFP4 = "nvfp4",
    MARLIN = "marlin",
    GGUF = "gguf",
    GPTQ_MARLIN_24 = "gptq_marlin_24",
    GPTQ_MARLIN = "gptq_marlin",
    AWQ_MARLIN = "awq_marlin",
    GPTQ = "gptq",
    COMPRESSED_TENSORS = "compressed-tensors",
    BITSANDBYTES = "bitsandbytes",
    QQQ = "qqq",
    HQQ = "hqq",
    EXPERTS_INT8 = "experts_int8",
    NEURON_QUANT = "neuron_quant",
    IPEX = "ipex",
    QUARK = "quark",
    MOE_WNA16 = "moe_wna16",
    TORCHAO = "torchao",
    NONE = "None"
}
/**
 * Tokenizer mode options
 */
export declare enum TokenizerMode {
    /**
     * “auto” will use the fast tokenizer if available.
     */
    AUTO = "auto",
    /**
     * “slow” will always use the slow tokenizer.
     */
    SLOW = "slow",
    /**
     * “mistral” will always use the mistral_common tokenizer.
     */
    MISTRAL = "mistral",
    /**
     * “custom” will use –tokenizer to select the preregistered tokenizer.
     */
    CUSTOM = "custom"
}
/**
 * Model config format options
 */
export declare enum ConfigFormat {
    /**
     * “auto” will try to load the config in hf format if available else it will try to load in mistral format
     */
    AUTO = "auto",
    HF = "hf",
    MISTRAL = "mistral"
}
/**
 * Tokenizer pool type options
 */
export declare enum TokenizerPoolType {
    RAY = "ray"
}
/**
 * Device type options for vLLM execution
 */
export declare enum Device {
    AUTO = "auto",
    CUDA = "cuda",
    NEURON = "neuron",
    CPU = "cpu",
    TPU = "tpu",
    XPU = "xpu",
    HPU = "hpu"
}
/**
 * LoRA data type options
 */
export declare enum LoraDtype {
    AUTO = "auto",
    FLOAT16 = "float16",
    BFLOAT16 = "bfloat16"
}
/**
 * Format options for rendering message content within a chat template
 */
export declare enum ChatTemplateContentFormat {
    AUTO = "auto",
    STRING = "string",
    OPENAI = "openai"
}
/**
 * Tool call parser options
 */
export declare enum ToolCallParser {
    GRANITE_20B_FC = "granite-20b-fc",
    GRANITE = "granite",
    HERMES = "hermes",
    INTERNLM = "internlm",
    JAMBA = "jamba",
    LLAMA3_JSON = "llama3_json",
    MISTRAL = "mistral",
    PHI4_MINI_JSON = "phi4_mini_json",
    PYTHONIC = "pythonic"
}
/**
 * Task options for model usage
 */
export declare enum VllmTask {
    AUTO = "auto",
    GENERATE = "generate",
    EMBEDDING = "embedding",
    EMBED = "embed",
    CLASSIFY = "classify",
    SCORE = "score",
    REWARD = "reward",
    TRANSCRIPTION = "transcription"
}
/**
 * Preemption mode.
 */
export declare enum PreemptionMode {
    RECOMPUTE = "recompute",
    SWAP = "swap"
}
/**
 * Scheduling policy options
 */
export declare enum SchedulingPolicy {
    FCFS = "fcfs",
    PRIORITY = "priority"
}
/**
 * VllmNamedArguments
 */
export interface VllmNamedArguments {
    /**
     * Host name.
     */
    readonly host?: string;
    /**
     * Port number.
     * @default 8000
     */
    readonly port?: number;
    /**
     * Log level for uvicorn.
     * @default UvicornLogLevel.INFO
     */
    readonly uvicornLogLevel?: UvicornLogLevel;
    /**
     * Disable uvicorn access log.
     * @default false
     */
    readonly disableUvicornAccessLog?: boolean;
    /**
     * Allow credentials.
     * @default false
     */
    readonly allowCredentials?: boolean;
    /**
     * Allowed origins.
     * @default ['*']
     */
    readonly allowedOrigins?: string[];
    /**
     * Allowed methods.
     * @default ['*']
     */
    readonly allowedMethods?: string[];
    /**
     * Allowed headers.
     * @default ['*']
     */
    readonly allowedHeaders?: string[];
    /**
     * If provided, the server will require this key to be presented in the header.
     */
    readonly apiKey?: string;
    /**
     * LoRA module configurations.
     * @example {"name": "name", "path": "lora_path", "base_model_name": "id"}
     */
    readonly loraModules?: {
        [key: string]: any;
    };
    /**
     * Prompt adapter configurations in the format name=path. Multiple adapters can be specified.
     */
    readonly promptAdapters?: string[];
    /**
     * The file path to the chat template, or the template in single-line form for the specified model.
     */
    readonly chatTemplate?: string;
    /**
     * The format to render message content within a chat template.
     * - “string” will render the content as a string.
     *   - Example: `"Hello World"`
     * - “openai” will render the content as a list of dictionaries, similar to OpenAI schema.
     *   - Example: `[{"type": "text", "text": "Hello world!"}]`
     * @default ChatTemplateContentFormat.AUTO
     */
    readonly chatTemplateContentFormat?: ChatTemplateContentFormat;
    /**
     * The role name to return if `request.add_generation_prompt=true`.
     * @default "assistant"
     */
    readonly responseRole?: string;
    /**
     * The file path to the SSL key file.
     */
    readonly sslKeyfile?: string;
    /**
     * The file path to the SSL cert file.
     */
    readonly sslCertfile?: string;
    /**
     * The CA certificates file.
     */
    readonly sslCaCerts?: string;
    /**
     * Refresh SSL Context when SSL certificate files change.
     * @default false
     */
    readonly enableSslRefresh?: boolean;
    /**
     * Whether client certificate is required (see stdlib ssl module's).
     * @default 0
     */
    readonly sslCertReqs?: number;
    /**
     * FastAPI root_path when app is behind a path based routing proxy.
     */
    readonly rootPath?: string;
    /**
     * 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, vLLM will add it to the server using `@app.middleware('http')`.
     * If a class is provided, vLLM will add it to the server using `app.add_middleware()`.
     * @default []
     */
    readonly middleware?: string[];
    /**
     * When `--max-logprobs` is specified,
     * represents single tokens as strings of the form 'token_id:{token_id}' so that tokens that are not JSON-encodable can be identified..
     * @default false
     */
    readonly returnTokensAsTokenIds?: boolean;
    /**
     * If specified, will run the OpenAI frontend server in the same process as the model serving engine.
     * @default false
     */
    readonly disableFrontendMultiprocessing?: boolean;
    /**
     * If specified, API server will add X-Request-Id header to responses.
     *
     * Caution: this hurts performance at high QPS.
     * @default false
     */
    readonly enableRequestIdHeaders?: boolean;
    /**
     * Enable auto tool choice for supported models.
     * Use `--tool-call-parser` to specify which parser to use.
     * @default false
     */
    readonly enableAutoToolChoice?: boolean;
    /**
     * Select the tool call parser depending on the model that you’re using.
     * This is used to parse the model-generated tool call into OpenAI API format.
     *
     * Required for `--enable-auto-tool-choice`.
     */
    readonly toolCallParser?: ToolCallParser;
    /**
     * Specify the tool parser plugin.
     * @default ""
     */
    readonly toolParserPlugin?: string;
    /**
     * Name or path of the huggingface model to use.
     * @default "facebook/opt-125m"
     */
    readonly model?: string;
    /**
     * The task to use the model for.
     * Each vLLM instance only supports one task, even if the same model can be used for multiple tasks.
     * When the model only supports one task, "auto" can be used to select it; otherwise,
     * you must specify explicitly which task to use.
     * @default VllmTask.AUTO
     */
    readonly task?: VllmTask;
    /**
     * Name or path of the huggingface tokenizer to use.
     * If unspecified, model name or path will be used.
     */
    readonly tokenizer?: string;
    /**
     * Name or path of the huggingface config to use.
     * If unspecified, model name or path will be used.
     */
    readonly hfConfigPath?: string;
    /**
     * Skip initialization of tokenizer and detokenizer.
     * Expects valid prompt_token_ids and None for prompt from the input.
     * The generated output will contain token ids.
     * @default false
     */
    readonly skipTokenizerInit?: boolean;
    /**
     * 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.
     */
    readonly revision?: string;
    /**
     * 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.
     */
    readonly codeRevision?: string;
    /**
     * Revision of the huggingface tokenizer to use.
     * It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.
     */
    readonly tokenizerRevision?: string;
    /**
     * The tokenizer mode.
     * @default TokenizerMode.AUTO
     */
    readonly tokenizerMode?: TokenizerMode;
    /**
     * Trust remote code from huggingface.
     * @default false
     */
    readonly trustRemoteCode?: boolean;
    /**
     * Allowing API requests to read local images or videos from directories specified by the server file system.
     * This is a security risk. Should only be enabled in trusted environments.
     */
    readonly allowedLocalMediaPath?: string;
    /**
     * The format of the model config to load.
     * @default ConfigFormat.AUTO
     */
    readonly configFormat?: ConfigFormat;
    /**
     * Data type for model weights and activations.
     * @default DataType.AUTO
     */
    readonly dtype?: DataType;
    /**
     * Model context length.
     */
    readonly maxModelLen?: number;
    /**
     * Optional regex pattern specifying valid logits processor qualified names that can be passed
     * with the logits_processors extra completion argument. Defaults to None, which allows no processors.
     */
    readonly logitsProcessorPattern?: string;
    /**
     * Which implementation of the model to use.
     * @default ModelImpl.AUTO
     */
    readonly modelImpl?: ModelImpl;
    /**
     * Disables sliding window, capping to sliding window size.
     * @default false
     */
    readonly disableSlidingWindow?: boolean;
    /**
     * Block manager v1 has been removed and SelfAttnBlockSpaceManager (i.e. block manager v2) is now the default.
     * @default true
     * @deprecated Setting this flag to True or False has no effect on vLLM behavior.
     */
    readonly useV2BlockManager?: boolean;
    /**
     * Random seed for operations.
     */
    readonly seed?: number;
    /**
     * Max number of log probs to return logprobs is specified in SamplingParams.
     * @default 20
     */
    readonly maxLogprobs?: number;
    /**
     * Disable logging statistics.
     * @default false
     */
    readonly disableLogStats?: boolean;
    /**
     * 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.
     */
    readonly quantization?: Quantization;
    /**
     * RoPE scaling configuration in JSON format.
     * @example {"rope_type":"dynamic","factor":2.0}
     */
    readonly ropeScaling?: {
        [key: string]: any;
    };
    /**
     * RoPE theta. Use with rope_scaling.
     * In some cases, changing the RoPE theta improves the performance of the scaled model.
     */
    readonly ropeTheta?: number;
    /**
     * The token to use as HTTP bearer authorization for remote files.
     * If provided, the Secret will be passed as HF_TOKEN secret to compile environment.
     */
    readonly hfToken?: Secret;
    /**
     * Extra arguments for the HuggingFace config.
     * This should be a object that will be parsed into a dictionary.
     */
    readonly hfOverrides?: {
        [key: string]: any;
    };
    /**
     * Always use eager-mode PyTorch.
     * If False, will use eager mode and CUDA graph in hybrid for maximal performance and flexibility.
     * @default false
     */
    readonly enforceEager?: boolean;
    /**
     * Maximum sequence length covered by CUDA graphs.
     * When a sequence has context length larger than this, we fall back to eager mode.
     * Additionally for encoder-decoder models, if the sequence length of the encoder input is larger than this,
     * we fall back to the eager mode.
     * @default 8192
     */
    readonly maxSeqLenToCapture?: number;
    /**
     * Overrides for the multimodal input mapping/processing, e.g., image processor.
     * @example {"num_crops": 4}
     */
    readonly mmProcessorKwargs?: {
        [key: string]: any;
    };
    /**
     * If true, then disables caching of the multi-modal preprocessor/mapper. (not recommended)
     * @default false
     */
    readonly disableMmPreprocessorCache?: boolean;
    /**
     * The pattern(s) to ignore when loading the model.Default to original/**\/* to avoid
     * repeated loading of llama’s checkpoints.
     * @default []
     */
    readonly ignorePatterns?: string[];
    /**
     * 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, metrics tag will take the first one.
     */
    readonly servedModelName?: string[];
    /**
     * Name or path of the QLoRA adapter.
     */
    readonly qloraAdapterNameOrPath?: string;
    /**
     * Enable deprecated Prometheus metrics that have been hidden since the specified version.
     * For example, if a previously deprecated metric has been hidden since the v0.7.0 release,
     * you use –show-hidden-metrics-for-version=0.7 as a temporary escape hatch while you migrate to new metrics.
     * The metric is likely to be removed completely in an upcoming release.
     */
    readonly showHiddenMetricsForVersion?: string;
    /**
     * Target URL to which OpenTelemetry traces will be sent.
     */
    readonly otlpTracesEndpoint?: string;
    /**
     * Valid choices are model,worker,all.
     * It makes sense to set this only if --otlp-traces-endpoint is set.
     * If set, it will collect detailed traces for the specified modules.
     * This involves use of possibly costly and or blocking operations and hence might have a performance impact.
     */
    readonly collectDetailedTraces?: string;
    /**
     * Disable async output processing. This may result in lower performance.
     * @default false
     */
    readonly disableAsyncOutputProc?: boolean;
    /**
     * The scheduler class to use.
     * @default "vllm.core.scheduler.Scheduler"
     */
    readonly schedulerCls?: string;
    /**
     * Override or set neuron device configuration.
     * @example {"cast_logits_dtype": "bloat16"}
     */
    readonly overrideNeuronConfig?: {
        [key: string]: any;
    };
    /**
     * Override or set the pooling method for pooling models.
     * @example {"pooling_type": "mean", "normalize": false}
     */
    readonly overridePoolerConfig?: {
        [key: string]: any;
    };
    /**
     * torch.compile configuration for the model.
     * When it is a number (0, 1, 2, 3), it will be interpreted as the optimization level.
     *
     * NOTE: level 0 is the default level without any optimization.
     * level 1 and 2 are for internal testing only. level 3 is the recommended level for production.
     * To specify the full compilation config, use a JSON string,
     * e.g. `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}` Following the convention of traditional compilers,
     * using -O without space is also supported. -O3 is equivalent to -O 3.
     */
    readonly compilationConfig?: {
        [key: string]: any;
    };
    /**
     * Configurations for distributed KV cache transfer in object.
     */
    readonly kvTransferConfig?: {
        [key: string]: any;
    };
    /**
     * The worker class to use for distributed execution.
     * @default "auto"
     */
    readonly workerCls?: string;
    /**
     * The worker extension class.
     * @default ""
     */
    readonly workerExtensionCls?: string;
    /**
     * The folder path to the generation config. Defaults to ‘auto’,
     * the generation config will be loaded from model path. If set to ‘vllm’,
     * no generation config is loaded, vLLM defaults will be used.
     * If set to a folder path, the generation config will be loaded from the specified folder path.
     * If max_new_tokens is specified in generation config,
     * then it sets a server-wide limit on the number of output tokens for all requests.
     * @default "auto"
     */
    readonly generationConfig?: string;
    /**
     * Overrides or sets generation config.
     * If used with –generation-config=auto, the override parameters will be merged with the default config from the model.
     * If generation-config is None, only the override parameters are used.
     * @example {"temperature": 0.5}
     */
    readonly overrideGenerationConfig?: {
        [key: string]: any;
    };
    /**
     * Enable sleep mode for the engine. (only cuda platform is supported)
     * @default false
     */
    readonly enableSleepMode?: boolean;
    /**
     * Additional config for specified platform.
     * Different platforms may support different configs.
     * Make sure the configs are valid for the platform you are using.
     * The input format is like ‘{“config_key”:”config_value”}’
     */
    readonly additionalConfig?: {
        [key: string]: any;
    };
    /**
     * Enable reasoning_content for the model.
     * @default false
     */
    readonly enableReasoning?: boolean;
    /**
     * Disable cascade attention for V1.
     * @default false
     */
    readonly disableCascadeAttn?: boolean;
    /**
     * Disable logging requests.
     * @default false
     */
    readonly disableLogRequests?: boolean;
    /**
     * Max number of prompt characters or prompt ID numbers in log.
     */
    readonly maxLogLen?: number;
    /**
     * Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint.
     * @default false
     */
    readonly disableFastApiDocs?: boolean;
    /**
     * Enable prompt_tokens_details in usage.
     * @default false
     */
    readonly enablePromptTokensDetails?: boolean;
    /**
     * Enable tracking server_load_metrics in the app state.
     * @default false
     */
    readonly enableServerLoadTracking?: boolean;
}
/**
 * Configuration for loading the model weights.
 */
export interface VllmLoadConfig {
    /**
     * 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 use CoreWeave’s tensorizer library for fast weight loading. See the Tensorize vLLM Model script in the Examples section for more information.
     * - “runai_streamer” will load the Safetensors weights using Run:ai Model Streamer.
     * - “bitsandbytes” will load the weights using bitsandbytes quantization.
     * - “sharded_state” will load weights from pre-sharded checkpoint files, supporting efficient loading of tensor-parallel models.
     * - “gguf” will load weights from GGUF format files (details specified in ggml-org/ggml).
     * - “mistral” will load weights from consolidated safetensors files used by Mistral models.
     * @default LoadFormat.AUTO
     */
    readonly loadFormat?: LoadFormat;
    /**
     * Directory to download and load the weights, default to the default cache directory of Hugging Face.
     */
    readonly downloadDir?: string;
    /**
     * Extra config for model loader. This will be passed to the model loader corresponding to the chosen load_format.
     * This should be a object that will be parsed into a dictionary.
     * @default {}
     */
    readonly modelLoaderExtraConfig?: {
        [key: string]: any;
    };
    /**
     * Whether to enable tqdm for showing progress bar when loading model weights.
     * @default true
     */
    readonly useTqdmOnLoad?: boolean;
}
/**
 * Dataclass which contains the decoding strategy of the engine.
 */
export interface VllmDecodingConfig {
    /**
     * Which engine will be used for guided decoding (JSON schema / regex etc) by default.
     * With “auto”, we will make opinionated choices based on request contents and what the backend libraries currently support,
     * so the behavior is subject to change in each release.
     * @default GuidedDecodingBackend.AUTO
     */
    readonly guidedDecodingBackend?: GuidedDecodingBackend;
    /**
     * Select the reasoning parser depending on the model that you’re using.
     * This is used to parse the reasoning content into OpenAI API format. Required for –enable-reasoning.
     */
    readonly reasoningParser?: ReasoningParser;
}
/**
 * Configuration for the distributed execution.
 */
export interface VllmParallelConfig {
    /**
     * Backend to use for distributed model workers, either “ray” or “mp” (multiprocessing).
     * If the product of pipeline_parallel_size and tensor_parallel_size is less than or equal to the number of GPUs available,
     * “mp” will be used to keep processing on a single host. Otherwise, this will default to “ray” if Ray is installed and fail otherwise.
     * Note that tpu and hpu only support Ray for distributed inference.
     */
    readonly distributedExecutorBackend?: DistributedExecutorBackend;
    /**
     * Number of pipeline parallel groups.
     * @default 1
     */
    readonly pipelineParallelSize?: number;
    /**
     * Number of tensor parallel groups.
     * @default 1
     */
    readonly tensorParallelSize?: number;
    /**
     * Number of data parallel groups.
     * MoE layers will be sharded according to the product of the tensor parallel size and data parallel size.
     * @default 1
     */
    readonly dataParallelSize?: number;
    /**
     * Use expert parallelism instead of tensor parallelism for MoE layers.
     * @default false
     */
    readonly enableExpertParallel?: boolean;
    /**
     * Maximum number of parallal loading workers when loading model sequentially in multiple batches.
     * To avoid RAM OOM when using tensor parallel and large models.
     */
    readonly maxParallelLoadingWorkers?: number;
    /**
     * Whether to profile Ray workers with nsight.
     * @see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler
     * @default false
     */
    readonly rayWorkersUseNsight?: boolean;
    /**
     * Disable the custom all-reduce kernel and fall back to NCCL.
     * @default false
     */
    readonly disableCustomAllReduce?: boolean;
}
/**
 * Configuration for the KV cache.
 */
export interface VllmCacheConfig {
    /**
     * Size of a contiguous cache block in number of tokens.
     * This is ignored on neuron devices and set to –max-model-len. On CUDA devices, only block sizes up to 32 are supported.
     * On HPU devices, block size defaults to 128.
     */
    readonly blockSize?: BlockSize;
    /**
     * The fraction of GPU memory to be used for the model executor, which can range from 0 to 1.
     * For example, a value of 0.5 would imply 50% GPU memory utilization.
     * If unspecified, will use the default value of 0.9. This is a per-instance limit,
     * and only applies to the current vLLM instance.
     * It does not matter if you have another vLLM instance running on the same GPU. For example,
     * if you have two vLLM instances running on the same GPU, you can set the GPU memory utilization to 0.5 for each instance.
     * @default 0.9
     */
    readonly gpuMemoryUtilization?: number;
    /**
     * Size of the CPU swap space per GPU (in GiB).
     * @default 4
     */
    readonly swapSpace?: number;
    /**
     * 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).
     * @default KvCacheDtype.AUTO
     */
    readonly kvCacheDtype?: KvCacheDtype;
    /**
     * Number of GPU blocks to use. This overrides the profiled num_gpu_blocks if specified.
     * Does nothing if None. Used for testing preemption.
     */
    readonly numGpuBlocksOverride?: number;
    /**
     * Whether to enable prefix caching. Disabled by default for V0. Enabled by default for V1.
     */
    readonly enablePrefixCaching?: boolean;
    /**
     * Set the hash algorithm for prefix caching.
     * @default PrefixCachingHashAlgo.BUILTIN
     */
    readonly prefixCachingHashAlgo?: PrefixCachingHashAlgo;
    /**
     * 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 is loaded from CPU memory to GPU memory on the fly in each model forward pass.
     * @default 0
     */
    readonly cpuOffloadGb?: number;
    /**
     * This enables dynamic calculation of k_scale and v_scale when kv_cache_dtype is fp8.
     * If False, the scales will be loaded from the model checkpoint if available. Otherwise, the scales will default to 1.0.
     * @default false
     */
    readonly calculateKvScales?: boolean;
}
/**
 * Controls the behavior of multimodal models.
 */
export interface VllmMultiModalConfig {
    /**
     * The maximum number of input items allowed per prompt for each modality.
     * This should be a object that will be parsed into a dictionary. Defaults to 1 (V0) or 999 (V1) for each modality.
     * @default {}
     */
    readonly limitMmPerPrompt?: {
        [key: string]: any;
    };
}
/**
 * Configuration for LoRA.
 */
export interface VllmLoraConfig {
    /**
     * If True, enable handling of LoRA adapters.
     * @default false
     */
    readonly enableLora?: boolean;
    /**
     * If True, enable bias for LoRA adapters.
     * @default false
     */
    readonly enableLoraBias?: boolean;
    /**
     * Max number of LoRAs in a single batch.
     * @default 1
     */
    readonly maxLoras?: number;
    /**
     * Max LoRA rank.
     * @default 16
     */
    readonly maxLoraRank?: number;
    /**
     * Maximum size of extra vocabulary that can be present in a LoRA adapter (added to the base model vocabulary).
     * @default 256
     */
    readonly loraExtraVocabSize?: number;
    /**
     * Data type for LoRA. If auto, will default to base model dtype.
     * @default LoraDtype.AUTO
     */
    readonly loraDtype?: LoraDtype;
    /**
     * Specify multiple scaling factors (which can be different from base model scaling factorsee 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.
     */
    readonly longLoraScalingFactors?: number;
    /**
     * Maximum number of LoRAs to store in CPU memory. Must be >= than max_loras.
     */
    readonly maxCpuLoras?: number;
    /**
     * 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.
     * @default false
     */
    readonly fullyShardedLoras?: boolean;
}
/**
 * Configuration for PromptAdapters.
 */
export interface VllmPromptAdapterConfig {
    /**
     * If True, enable handling of PromptAdapters.
     * @default false
     */
    readonly enablePromptAdapter?: boolean;
    /**
     * Max number of PromptAdapters in a batch.
     * @default 1
     */
    readonly maxPromptAdapters?: number;
    /**
     * Max number of PromptAdapters tokens.
     * @default 0
     */
    readonly maxPromptAdapterToken?: number;
}
export interface VllmDeviceConfig {
    /**
     * Device type for vLLM execution.
     * @default Device.AUTO
     */
    readonly device?: Device;
}
/**
 * Configuration for speculative decoding.
 */
export interface VllmSpeculativeConfig {
    /**
     * The configurations for speculative decoding. Should be a object.
     */
    readonly speculativeConfig?: {
        [key: string]: any;
    };
}
export interface VllmSchedulerConfig {
    /**
     * Maximum number of tokens to be processed in a single iteration.
     *
     * This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context.
     */
    readonly maxNumBatchedTokens?: number;
    /**
     * Maximum number of sequences to be processed in a single iteration.
     *
     * This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context.
     */
    readonly maxNumSeqs?: number;
    /**
     * For chunked prefill, the maximum number of sequences that can be partially prefilled concurrently.
     * @default 1
     */
    readonly maxNumPartialPrefills?: number;
    /**
     * For chunked prefill, the maximum number of prompts longer than long_prefill_token_threshold that will be prefilled concurrently.
     * Setting this less than max_num_partial_prefills will allow shorter prompts to jump the queue in front of longer prompts in some cases, improving latency.
     * @default 1
     */
    readonly maxLongPartialPrefills?: number;
    /**
     * For chunked prefill, a request is considered long if the prompt is longer than this number of tokens.
     * @default 0
     */
    readonly longPrefillTokenThreshold?: number;
    /**
     * The number of slots to allocate per sequence per step,
     * beyond the known token ids. This is used in speculative decoding to store KV activations of tokens
     * which may or may not be accepted.
     *
     * NOTE: This will be replaced by speculative config in the future; it is present to enable correctness tests until then.
     * @default 0
     */
    readonly numLookaheadSlots?: number;
    /**
     * Apply a delay (of delay factor multiplied by previous prompt latency) before scheduling next prompt.
     * @default 0.0
     */
    readonly schedulerDelayFactor?: number;
    /**
     * Whether to perform preemption by swapping or recomputation.
     * If not specified, we determine the mode as follows:
     * We use recomputation by default since it incurs lower overhead than swapping.
     * However, when the sequence group has multiple sequences (e.g., beam search),
     * recomputation is not currently supported. In such a case, we use swapping instead.
     */
    readonly preemptionMode?: PreemptionMode;
    /**
     * Maximum number of forward steps per scheduler call.
     * @default 1
     */
    readonly numSchedulerSteps?: number;
    /**
     * If False, then multi-step will stream outputs at the end of all steps
     * @default true
     */
    readonly multiStepStreamOutputs?: boolean;
    /**
     * The scheduling policy to use:
     * - “fcfs” means first come first served, i.e. requests are handled in order of arrival.
     * - “priority” means requests are handled based on given priority (lower value means earlier handling) and time of arrival deciding any ties).
     * @default SchedulingPolicy.FCFS
     */
    readonly schedulingPolicy?: SchedulingPolicy;
    /**
     * If True, prefill requests can be chunked based on the remaining max_num_batched_tokens.
     */
    readonly enableChunkedPrefill?: boolean;
    /**
     * If set to true and chunked prefill is enabled, we do not want to partially schedule a multimodal item.
     * Only used in V1 This ensures that if a request has a mixed prompt (like text tokens TTTT followed by image tokens IIIIIIIIII)
     * where only some image tokens can be scheduled (like TTTTIIIII, leaving IIIII),
     * it will be scheduled as TTTT in one step and IIIIIIIIII in the next.
     * @default false
     */
    readonly disableChunkedMmInput?: boolean;
}
/**
 * Interface for vLLM server command line arguments
 */
export interface VllmEngineArguments extends VllmNamedArguments, VllmLoadConfig, VllmDecodingConfig, VllmParallelConfig, VllmCacheConfig, VllmMultiModalConfig, VllmLoraConfig, VllmPromptAdapterConfig, VllmDeviceConfig, VllmSpeculativeConfig, VllmSchedulerConfig {
}
export declare abstract class VllmEngineArgumentsParser {
    /**
     * Convert vLLM engine arguments (camel case) to config (kebab case)
     * @param args vLLM engine arguments
     * @returns vLLM engine config
     * @see https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#configuration-file
     */
    static config(args: VllmEngineArguments): {
        [x: string]: any;
    };
    static cli(args: VllmEngineArguments): string[];
}