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  • Engine Arguments - vLLM
    Home User Guide Configuration Engine Arguments Engine arguments control the behavior of the vLLM engine For offline inference, they are part of the arguments to LLM class For online serving, they are part of the arguments to vllm serve The engine argument classes, EngineArgs and AsyncEngineArgs, are a combination of the configuration classes defined in vllm config Therefore, if you are
  • Engine Arguments — vLLM
    Engine Arguments # Engine arguments control the behavior of the vLLM engine For offline inference, they are part of the arguments to LLM class For online serving, they are part of the arguments to vllm serve Below, you can find an explanation of every engine argument:
  • Engine Arguments — vLLM - Read the Docs
    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
  • Chapter 2. Complete list of vLLM server arguments | vLLM server . . .
    The following is a comprehensive list of the vLLM server arguments that you can use with the vllm serve command An explanation of each server argument and default values is provided
  • Chapter 3. vLLM server usage | vLLM server arguments | Red Hat AI . . .
    Chapter 3 vLLM server usage The vllm command provides subcommands for starting the inference server, generating chat and text completions, running benchmarks, and executing batch prompts
  • vLLM Engine Arguments | Unsloth Documentation
    Basics 🖥️ Inference Deployment vLLM Deployment Inference Guide vLLM Engine Arguments vLLM engine arguments, flags, options for serving models on vLLM
  • vllm vllm engine arg_utils. py at main · vllm-project vllm · GitHub
    A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project vllm
  • vllm docs at main · vllm-project vllm · GitHub
    A high-throughput and memory-efficient inference and serving engine for LLMs - vllm docs at main · vllm-project vllm
  • vllm. engine. llm_engine — vLLM - Read the Docs
    [docs] class LLMEngine: """An LLM engine that receives requests and generates texts This is the main class for the vLLM engine It receives requests from clients and generates texts from the LLM It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache) This class utilizes iteration-level
  • Models - Engine Arguments - 《vLLM v0. 4. 1 Documentation . . . - 书栈网
    Async Engine Arguments Below are the additional arguments related to the asynchronous engine: Named Arguments --engine-use-ray Use Ray to start the LLM engine in a separate process as the server process --disable-log-requests Disable logging requests --max-log-len Max number of prompt characters or prompt ID numbers being printed in log
  • Models - Engine Arguments - 《vLLM v0. 4. 3 Documentation . . . - 书栈网
    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 --max-cpu-loras Maximum number of LoRAs to store in CPU memory Must be >= than max_num_seqs Defaults to max_num_seqs --fully-sharded-loras





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