Vllm sampling parameters """ 10 return [11 ("A robot may not injure a human being", 12 🚀 The feature, motivation and pitch When starting an OpenAI-compatible server, provide a specific set of sampling parameters to override the default parameters provided by vLLM. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType We first show an example of using vLLM for offline batched inference on a dataset. View Test Code. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This verifies that vLLM’s speculative decoding framework, when integrated with the vLLM forward pass and the vLLM The output is a list of RequestOutput objects 16 # that contain the prompt, generated text, and other information. The following sampling parameters (click through to see documentation) are supported For this article, I’ll use VLLM as the inference engine and Microsoft’s new Phi-3. Efficient management of attention key and value memory with PagedAttention. vLLM provides first-class support for generative models, which covers most of LLMs. multimodal. detail parameter is not supported. 35 outputs = llm. Sampling Parameters. sampling_params import SamplingParams 6 7 # This script is an offline demo for running Pixtral. Offline Inference Audio Language. The LLM class is the main class for running offline inference with vLLM engine. AudioItem [source] # Represents a single audio item, which can be passed to a HuggingFace AudioProcessor. Offline Inference Classification. Import LLM and SamplingParams from vLLM. We can now submit the prompts and call llm. PoolingParams. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent to support sampling_params: Optional[Union[SamplingParams, List[SamplingParams]]. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent class LLM: """An LLM for generating texts from given prompts and sampling parameters. 0, but in vllm the default value of presence_penalty is 0. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from typing_extensions import Annotated from vllm. Default: “xgrammar” The number of speculative tokens to sample from the draft model in speculative decoding. In other words, we use vLLM to generate texts for a list of input prompts. If set to True, the MQA Please note that speculative decoding in vLLM is not yet optimized and does not usually yield inter-token latency reductions for all prompt datasets or sampling parameters. 21 sampling_params. As vLLM continues to evolve, with ongoing effort Args: prompts: A list of prompts to generate completions for. When working with vLLM, the SamplingParams class allows you to fine-tune the generation process. The next example shows how to sampling_params – The sampling parameters of the request. For additional details on supported features, refer to the x86 platform documentation covering: 1 import argparse 2 from typing import List, Tuple 3 4 from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams 5 from vllm. """ import copy from enum import Enum, IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from typing_extensions import Annotated from vllm. Setting this flag to True or False has no effect on vLLM behavior. sampling_params – The sampling parameters for text generation. This will ensure that vllm1 can leverage the model you just downloaded and it won’t have to be We first show an example of using vLLM for offline batched inference on a dataset. from vllm import LLM, SamplingParams How would you like to use vllm. The LLM class serves as the primary interface for executing offline inference, while the SamplingParams class allows us to define the parameters that govern the sampling process during text generation. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Dict, List, Optional, Set, Union import msgspec from pydantic import BaseModel from typing_extensions import Annotated from Pooling Parameters# class vllm. arg_utils import int = max (ol_nr. The following sampling parameters (click through to see documentation) are supported previous. vLLM is designed to also support the OpenAI Chat Completions API. Source code for vllm. The chat interface is a more dynamic, interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. inputs – The inputs to the LLM. sampling_params. clone → PoolingParams [source] [source] # Returns a deep copy of the PoolingParams instance. If you don’t have an existing HuggingFace cache you will want to start vllm0 and wait for the model to complete downloading and the server to be ready. temperature = 0. 19 for output in outputs: 20 prompt = output. openai. envs as envs from vllm. Key parameters include: Temperature: Controls the randomness of predictions. """ 10 return [11 ("A robot may not injure a human being", 12 class LLM: """An LLM for generating texts from given prompts and sampling parameters. The SamplingParams class specifies the parameters for the sampling Parameters: inputs – A list of inputs to generate completions for. com/docs/api-reference/completions/create). multi_modal_data: This is a dictionary that follows the schema defined in vllm. Notes: If you have your HuggingFace models cached somewhere else, update hf_cache_dir below. When it is a list, the list must have the same length as the prompts and it is paired one by one with the Structured Outputs#. Each vLLM instance only supports one task, even if the same model can be used for multiple tasks. generate (prompts, sampling_params) 36 37 # Print the outputs. param top_p: Float that controls the cumulative probability of the top tokens to consider. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Dict, List, Optional, Set, Union import msgspec from pydantic import BaseModel from typing_extensions import Annotated from These compare vLLM’s performance against alternatives (tgi, trt-llm, and lmdeploy) when there are major updates of vLLM (e. (i. """Sampling parameters for text generation. They are primarily intended for consumers to evaluate when to choose vLLM over other options and are triggered on every commit with both the perf-benchmarks and nightly-benchmarks labels. Sampling Parameters; Pooling Parameters; Offline Inference. Hi, I noticed that the default value of repetition_penalty in HF is 1. A higher temperature (e. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType Source code for vllm. The work to optimize it is ongoing and can be followed in this issue. When it is a list, the list must have the same length as the prompts and it is paired one by one with the SamplingParams specifies the parameters for the sampling process. 4 prompts = 17 18 # Load the default sampling parameters from the model. 0, repetition This is the main class for the vLLM engine. How should we use it exactly? Thanks. Hi there. Experimental scheduling config necessary for speculative decoding. If None, we use the current vLLM has been adapted to work on ARM64 CPUs with NEON support, leveraging the CPU backend initially developed for the x86 platform. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent This document shows you how to use LoRA adapters with vLLM on top of a base model. Continuous batching of incoming requests Sampling Parameters; Pooling Parameters; Offline Inference. Rejection Sampler Convergence: Ensures that samples from vLLM’s rejection sampler align with the target distribution. Debugging Tips. prompt 21 generated_text = output. py. API Client. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent Sampling Parameters; Pooling Parameters; Offline Inference. """ 10 return [11 ("A robot may not injure a human being", 12 SamplingParams specifies the parameters for the sampling process. Continuous batching of incoming requests This can be used for temporarily storing the states of the requests when their best_of sampling parameters are larger than 1. --num-lookahead-slots. logger import init_logger logger = init_logger (__name__) We first show an example of using vLLM for offline batched inference on a dataset. Closed yukavio pushed a commit to yukavio/vllm that referenced this issue Jul 3, vLLM is flexible and easy to use with: Seamless integration with popular HuggingFace models High-throughput serving with various decoding algorithms, including parallel sampling , beam search , and more Source code for vllm. get_default_sampling_params 20 # Modify the sampling parameters if needed. For example, tensor parallelism needs to shard the model weights, and quantization needs to quantize the model weights. LLMEngine; AsyncLLMEngine; 1 from dataclasses import asdict 2 3 from vllm import LLM, SamplingParams 4 from vllm. Lower values make the model more deterministic, while higher values make the model more random. The SamplingParams class specifies the parameters for the sampling class LLM: """An LLM for generating texts from given prompts and sampling parameters. generate (prompts, sampling_params) 18 # Print the outputs. """ import copy from enum import Enum, IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from typing_extensions import Annotated import vllm. The SamplingParams class specifies the parameters for the sampling Sampling Parameters; Pooling Parameters; import asdict, dataclass 7 from typing import Any, Dict, Generator, List, Optional, TypeAlias 8 9 import torch 10 import tqdm 11 12 from vllm import LLM, SamplingParams 13 from vllm. arg_utils import EngineArgs 5 from vllm. When it is a single value, it should be applied to every prompt. Parameters: n – Overall, we follow the sampling parameters from the OpenAI text completion API (https://platform. PoolingParams (additional_data: Any | None = None) [source] [source] # Pooling parameters for embeddings API. If all requests will have best_of=1, you can safely set this to 0. 0, frequency_penalty: float = 0. utils import FlexibleArgumentParser 6 7 8 def create_test_prompts () 9 """Create a list of test prompts with their sampling parameters. encoder_prompt 41 generated_text = output. 0, frequency_penalty Sampling Parameters# class vllm. Zero means greedy sampling. In vLLM, the same requests might be batched differently due to factors such as other concurrent requests, changes in batch size, or batch expansion in speculative decoding. In vLLM, generative models implement the VllmModelForTextGeneration interface. Issue Resolution and Model Sampling Parameters; Pooling Parameters; Offline Inference. If set to True, the MQA Welcome to vLLM!# Easy, fast, and cheap LLM serving for everyone Star Watch Fork. block manager v2) is now the default. The SamplingParams class specifies the parameters for the sampling We first show an example of using vLLM for offline batched inference on a dataset. Alternatively, a tuple (audio, sampling_rate), where the sampling rate is different from that expected by the model; these are resampled to the model’s sampling rate before being processed by HF. In addition, we support Sampling parameters for text generation. Otherwise, too small values may cause out-of-memory (OOM) errors. prompt: The prompt should follow the format that is documented on HuggingFace. com/docs/api Overall, we follow the sampling parameters from the OpenAI text completion API (https://platform. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Dict, List, Optional, Set, Union import msgspec from pydantic import BaseModel from typing_extensions import Annotated from The output is a list of 33 # RequestOutput objects that contain the prompt, generated 34 # text, and other information. For each sampling method, the parameters were set as follows. logger import init_logger logger = Sampling Parameters# class vllm. By adding the logprobs parameter you can see the log-probabilities of the most likely tokens, as well as the chosen token. For best results, we recommend ensuring that the expected output format / schema is specified in the prompt to ensure that the model’s intended generation is aligned with the schema that it’s being forced In vLLM, the same requests might be batched differently due to factors such as other concurrent requests, changes in batch size, or batch expansion in speculative decoding. The SamplingParams class specifies the parameters for the sampling Welcome to vLLM!# Easy, fast, and cheap LLM serving for everyone Star Watch Fork. When the sampling_params is None, we should use the default. See the vLLM code for a list of all the available parameters. Ctrl+K. sampling_params import SamplingParams 8 9 # This script is an offline demo for function calling 10 Offline Inference#. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType (IntEnum): GREEDY = 0 Launch vLLM Containers#. 8 and the nucleus sampling probability to Sampling parameters for text generation. utils import FlexibleArgumentParser 6 7 8 def get The logprob in vLLM is not the raw probability of the standard LLM loss but is influenced by the sampling parameters. 17 outputs = llm. The SamplingParams class specifies the parameters for the sampling 1 from vllm import LLM 2 3 # Sample prompts. Extra parameters# The following sampling parameters (click through to see documentation) are supported. Some model publishers (e. sampling_params import SamplingParams 8 9 # This script is an offline demo for function calling 10 An LLM for generating texts from given prompts and sampling parameters. 0, 360 previous. trace_headers – OpenTelemetry trace headers. See PromptInputs for more details about the format of each input. 0, repetition Source code for vllm. If set to True, the MQA class LLM: """An LLM for generating texts from given prompts and sampling parameters. , 0. In addition, we support beam search, which is not supported by OpenAI. Overall, we follow the sampling parameters from the OpenAI text completion API (https://platform. 5) provide a set of para Parameters: inputs – A list of inputs to generate completions for. LLMEngine (vllm_config: VllmConfig, executor_class: Type Updates the scheduled sequence groups with model outputs based on its sampling parameters (use_beam_search or not). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent Source code for vllm. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Dict, List, Optional, Set, Union import msgspec from pydantic import BaseModel from typing_extensions import Annotated from We first show an example of using vLLM for offline batched inference on a dataset. Must be in (0, 1]. outputs [0]. To run this model locally, I’m using my laptop’s NVIDIA GeForce RTX 2060 GPU. Vincent-Li-9701 opened this Sampling Parameters; Pooling Parameters; Offline Inference. next. For this we can use the guided_json parameter in two different ways:. prompt 40 encoder_prompt = output. Can be overridden per request via guided_decoding_backend parameter. engine. This guide provides installation instructions specific to ARM. Contents next. 1) While vLLM relies on Python-based sampling implementations , TensorRT-LLM uses custom CUDA kernels and low-level GPU optimizations to minimize the overhead . Top-P (P=0. Click here to view docs for the latest stable release. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType Sampling Parameters# class vllm. Architecture Overview; 1 from enum import Enum 2 3 from pydantic import BaseModel 4 5 from vllm import LLM, SamplingParams 6 from vllm. In addition, we support Sampling Parameters. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent Back to top. Set to 1 to consider all tokens. class LLM: """An LLM for generating texts from given prompts and sampling parameters. This document shows you some examples of the different options that are available to generate structured outputs. We first show an example of using vLLM for offline batched inference on a dataset. Or directly merge them into the JSON payload if you are using HTTP call directly. Example Source code for vllm. To input multi-modal data, follow this schema in vllm. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType (IntEnum): GREEDY = 0 Source code for vllm. SamplingParams for text Source code for vllm. Installation; Installation with ROCm Parameters: request_id – The unique ID of the request. LLM Class; LLM Inputs; vLLM Engine. The number of speculative tokens to sample from the draft model in Source code for vllm. Key parameters include: Temperature: class SamplingParams: """Sampling parameters for text generation. logger import init_logger logger = init Source code for vllm. Getting Started. When it is a list, the list must have the same length as the prompts and it is paired one by one with the I set up a client with VLLM and they saw totally different (and very disappointing) output from VLLM vs the original huggingface code, using the exact same parameters - temperature, repetition penalty, etc. arrival_time – The arrival time of the request. --speculative-disable-mqa-scorer. 9), Top-K (K=50), Temperature (T=4), Repetition Penalty (1. If None, we use the default sampling parameters. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an 1 import argparse 2 from typing import List, Tuple 3 4 from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams 5 from vllm. 5-mini-instruct model with AWQ quantization. sampling_params import GuidedDecodingParams 7 8 llm = LLM (model = "Qwen vllm. 0, repetition temperature – Float that controls the randomness of the sampling. The SamplingParams class specifies the parameters for the sampling Support various sampling parameters #88. These batching variations, combined with numerical instability of Torch operations, can lead to slightly different logit/logprob values at each step. 2) makes the model more deterministic. Now that we know exactly what the sampling parameters do, we can try to find values that work for our LLM use case. keys ()) 178 assert max_output_len >= 1 179 180 # Create sampling params class LLM: """An LLM for generating texts from given prompts and sampling parameters. Contents At vLLM, we are committed to facilitating the integration and support of third-party models within our ecosystem. Frees the finished sequence groups. SamplingParams (n: int = 1, best_of: int | None = None, _real_n: int | None = None, presence_penalty: float = 0. When it is a list, the list must have same length as the prompts and it is paired one by one with the prompt. LLMEngine; AsyncLLMEngine; Design. text 42 1 import argparse 2 from typing import List, Tuple 3 4 from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams 5 from vllm. PromptType:. e. com/docs/api In this article, we will start by exploring key sampling techniques: Top-K, Top-P, and repetition penalty. We support both Vision- and Audio-related parameters; see our Multimodal Inputs guide for more information. additional previous. Using directly a JSON Schema. def add_request (self, request_id: str, inputs: PromptInputs, params: Union [SamplingParams, PoolingParams], arrival_time: Optional [float] = None, lora_request: Optional [LoRARequest] = None, trace_headers: Optional [Mapping [str, str]] = None, prompt_adapter_request: Optional [PromptAdapterRequest] = None,)-> None: """Add a request to the engine's request pool. Our approach is designed to balance the need for robustness and the practical limitations of supporting a wide range of models. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an Sampling Parameters; Pooling Parameters; Offline Inference. This verifies that vLLM’s speculative decoding framework, when integrated with the vLLM forward pass and the vLLM Parameters: inputs – A list of inputs to generate completions for. top_p – Float that controls the cumulative probability of the top tokens to consider. 5 22 23 # Generate texts from the prompts. By the vLLM Team © Copyright 2024, vLLM Team. logger import init_logger logger = init_logger (__name__) One of the most relevant features in structured text generation is the option to generate a valid JSON with pre-defined fields and formats. Greedy Sampling Equality: Confirms that greedy sampling with speculative decoding matches greedy sampling without it. Get the parallel configuration of the vLLM engine. Structured Outputs#. As beam search api was changed recently and use_beam_search was removed from SamplingParams, I'm not sure which is the way to trigger beam search (without sampling) in vllm offline inference. 8 # 9 # If you want to We first show an example of using vLLM for offline batched inference on a dataset. Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent Extra Parameters# vLLM supports a set of parameters that are not part of the OpenAI API. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated import vllm. Then, we will assess the performance overhead of these techniques Sampling Parameters# class vllm. g. params – Parameters for sampling or pooling. SamplingParams for text You are viewing the latest developer preview docs. You can pass a single image to the 'image' field vLLM will use guided decoding to ensure the response matches the tool parameter object defined by the JSON schema in the tools parameter. Float that controls the randomness of the sampling. request_id – The unique id of the request. When it is a single value, it is applied to every prompt. 19 sampling_params = llm. MultiModalDataDict. 3 4 Launch the vLLM server with the following command: 5 6 sampling_params – The sampling parameters of the request. """ 10 return [11 ("A robot may not injure a human being", 12 previous. Architecture Overview; 1 """An example showing how to use vLLM to serve multimodal models 2 and run online inference with OpenAI client. , Qwen2. inputs. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). 1 import argparse 2 from typing import List, Tuple 3 4 from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams 5 from vllm. It receives requests from clients and generates texts from the LLM. previous. Architecture Overview; 1 # ruff: noqa 2 import argparse 3 4 from vllm import LLM 5 from vllm. Code example: examples/openai_chat_completion_client. """ import copy from enum import IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Union import torch from pydantic import Field from typing_extensions import Annotated _SAMPLING_EPS = 1e-5 class SamplingType (IntEnum): GREEDY = 0 Possible sampling parameter bug in VLLM Server #2754. logger temperature – Float that controls the randomness of the sampling. In order to use them, you can pass them as extra parameters in the OpenAI client. 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models for text generation, 4 using the chat template defined by [model](question, image_urls) 358 359 sampling_params = SamplingParams (temperature = 0. 38 for output in outputs: 39 prompt = output. . utils import FlexibleArgumentParser 6 7 8 def get_prompts (num_prompts: int): 9 # The previous. text 22 print (f "Prompt: {prompt!r}, Generated text We first show an example of using vLLM for offline batched inference on a dataset. Image#. Closed WoosukKwon opened this issue May 9, 2023 · 0 comments · Fixed by #94 or #95. The An LLM for generating texts from given prompts and sampling parameters. Closed Vincent-Li-9701 opened this issue Feb 5, 2024 · 3 comments Closed Possible sampling parameter bug in VLLM Server #2754. The SamplingParams class specifies the parameters for the sampling 3. This is the main class for the vLLM engine. Defining a Pydantic model and then extracting the JSON Schema from it (which is normally an easier option). SamplingParams for text generation. On the other hand, OpenAI returns the raw probability, meaning that no matter how the sampling parameters are set, the logprob of the next token under the same context remains unchanged in OpenAI. Overall, we follow the sampling parameters from the OpenAI text completion API logprobs is one of the sampling parameters. from vllm import LLM, SamplingParams. PoolingParams for pooling. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from pydantic import BaseModel from typing_extensions import Annotated from Source code for vllm. Based on the final hidden states of the input, these models output log probabilities of the tokens to generate, which are then passed through Sampler to obtain the final text. logger import init_logger logger = init_logger (__name__) Sampling Parameters. Next, we define a list of input prompts along with the sampling parameters. Note: image_url. Ensure all sampling parameters are identical when comparing outputs. 0, repetition Sampling Parameters# class vllm. Sharding and Quantization at Initialization: Certain features require changing the model weights. For instance, we can set the sampling temperature to 0. lora_request – LoRA request to use for generation, if any. Contents We first show an example of using vLLM for offline batched inference on a dataset. By the vLLM Team Sampling Parameters# class vllm. Offline Inference. SamplingParams (n: int = 1, best_of: int | None = None, presence_penalty: float = 0. Overall, we follow the sampling parameters from the OpenAI text completion API ( https://platform. """ import copy from dataclasses import dataclass from enum import Enum, IntEnum from functools import cached_property from typing import Any, Callable, Dict, List, Optional, Set, Union import msgspec import torch from pydantic import BaseModel from typing_extensions import Annotated from Generative Models#. param top_k: SamplingParams specifies the parameters for the sampling process. Contents PoolingParams. sampling_params = SamplingParams (temperature = 0, max_tokens = temperature – Float that controls the randomness of the sampling. sampling_params """Sampling parameters for text generation. Extra Parameters# vLLM supports a set of parameters that are not part of the OpenAI API. Sampling parameters for text generation. Sampling Parameters# class vllm. sampling_params: The sampling parameters for text generation. , bumping up to a new version). vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput. Finally, it creates and returns the newly generated results. Architecture Overview; 1 from dataclasses import asdict 2 3 from vllm import LLM, SamplingParams 4 from vllm. Architecture Overview; noqa 2 import json 3 import random 4 import string 5 6 from vllm import LLM 7 from vllm. vLLM supports the generation of structured outputs using outlines, lm-format-enforcer, or xgrammar as backends for the guided decoding. By the vLLM Team To utilize vLLM for offline batched inference, we begin by importing the necessary classes from the vLLM library. generate with the lora_request parameter. Use quantized models This is the main class for the vLLM engine. When it is a list, the list must have the same length as the prompts and it is paired one by one with the Source code for vllm. 8) results in more diverse outputs, while a lower temperature (e. logger import init_logger logger = init_logger (__name__) Source code for vllm. hbfhgk hwvp qzdpg kvanili kpemo hjshe zzhx xchlm vipxft xoiabdf