🚀 | This serverless worker utilizes vLLM behind the scenes and is integrated into RunPod's serverless environment. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature.

Setting up the Serverless Worker
Usage

Setting up the Serverless Worker

Option 1: Deploy Any Model Using Pre-Built Docker Image

We now offer a pre-built Docker Image for the vLLM Worker that you can configure entirely with Environment Variables when creating the RunPod Serverless Endpoint:

Stable Image: runpod/worker-vllm:0.1.0

Development Image: runpod/worker-vllm:dev

Prerequisites

RunPod Account

Environment Variables

Required:
- MODEL_NAME: Hugging Face Model Repository (e.g., openchat/openchat-3.5-1210).
Optional:
- MAX_MODEL_LENGTH: Maximum number of tokens for the engine to be able to handle. (default: maximum supported by the model)
- MODEL_BASE_PATH: Model storage directory (default: /runpod-volume).
- HF_TOKEN: Hugging Face token for private and gated models (e.g., Llama, Falcon).
- NUM_GPU_SHARD: Number of GPUs to split the model across. (default: 1)
- QUANTIZATION: AWQ (awq), SqueezeLLM (squeezellm) or GPTQ (gptq) Quantization. The specified Model Repo must be of a quantized model. (default: None)
- MAX_CONCURRENCY: Max concurrent requests. (default: 100)
- DEFAULT_BATCH_SIZE: Token streaming batch size (default: 30). This reduces the number of HTTP calls, increasing speed 8-10x vs non-batching, matching non-streaming performance.
- DISABLE_LOG_STATS: Enable (0) or disable (1) vLLM stats logging.
- DISABLE_LOG_REQUESTS: Enable (0) or disable (1) request logging.

Option 2: Build Docker Image with Model Inside

To build an image with the model baked in, you must specify the following docker arguments when building the image.

Prerequisites

Docker
Linux
NVIDIA GPU

Note

We will be adding support for building on any OS without a GPU.

Arguments:

Required
- MODEL_NAME
Optional
- MODEL_BASE_PATH: Defaults to /runpod-volume for network storage. Use /models or for local container storage.
- QUANTIZATION
- WORKER_CUDA_VERSION: 11.8 or 12.1 (default: 11.8 due to a small amount of workers not having CUDA 12.1 support yet. 12.1 is recommended for optimal performance).

Example: Building an image with OpenChat-3.5

sudo docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg MODEL_BASE_PATH="/models" .

(Optional) Including Huggingface Token

If the model you would like to deploy is private or gated, you will need to include it during build time as a Docker secret, which will protect it from being exposed in the image and on DockerHub.

Enable Docker BuildKit (required for secrets).

export DOCKER_BUILDKIT=1

Export your Hugging Face token as an environment variable

export HF_TOKEN="your_secret_value_here"

Add the token as a secret when building

docker build -t username/image:tag --secret id=HF_TOKEN --build-arg MODEL_NAME="openchat/openchat_3.5" .

Compatible Models

LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
GPT-2 (gpt2, gpt2-xl, etc.)
GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
Phi (microsoft/phi-1_5, microsoft/phi-2, etc.)
Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

And any other models supported by vLLM 0.2.6.

Ensure that you have Docker installed and properly set up before running the docker build commands. Once built, you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand. For further inquiries or assistance, feel free to contact our support team.

Usage

Endpoint Model Inputs

You may either use a prompt or a list of messages as input. If you use messages, the model's chat template will be applied to the messages automatically, so the model must have one. If you use prompt, you may optionally apply the model's chat template to the prompt by setting apply_chat_template to true.

Argument	Type	Default	Description
`prompt`	str		Prompt string to generate text based on.
`messages`	list[dict[str, str]]		List of messages, which will automatically have the model's chat template applied. Overrides `prompt`.
`apply_chat_template`	bool	False	Whether to apply the model's chat template to the `prompt`.
`sampling_params`	dict	{}	Sampling parameters to control the generation, like temperature, top_p, etc.
`stream`	bool	False	Whether to enable streaming of output. If True, responses are streamed as they are generated.
`batch_size`	int	DEFAULT_BATCH_SIZE	The number of tokens to stream every HTTP POST call.

Text Input Formats

You may either use a prompt or a list of messages as input.

1. `prompt`

The prompt string can be any string, and the model's chat template will not be applied to it unless apply_chat_template is set to true, in which case it will be treated as a user message.

Example:

"prompt": "..."

2. `messages`

Your list can contain any number of messages, and each message can have any role from the following list:

user
assistant
system

The model's chat template will be applied to the messages automatically, so the model must have one.

Example:

"messages": [
    {
      "role": "system",
      "content": "..."
    },
    {
      "role": "user",
      "content": "..."
    },
    {
      "role": "assistant",
      "content": "..."
    }
  ]

Sampling Parameters

Argument	Type	Default	Description
`best_of`	Optional[int]	None	Number of output sequences generated from the prompt. The top `n` sequences are returned from these `best_of` sequences. Must be ≥ `n`. Treated as beam width in beam search. Default is `n`.
`presence_penalty`	float	0.0	Penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
`frequency_penalty`	float	0.0	Penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.
`repetition_penalty`	float	1.0	Penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens, values < 1 encourage repetition.
`temperature`	float	1.0	Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.
`top_p`	float	1.0	Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
`top_k`	int	-1	Controls the number of top tokens to consider. Set to -1 to consider all tokens.
`min_p`	float	0.0	Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.
`use_beam_search`	bool	False	Whether to use beam search instead of sampling.
`length_penalty`	float	1.0	Penalizes sequences based on their length. Used in beam search.
`early_stopping`	Union[bool, str]	False	Controls stopping condition in beam search. Can be `True`, `False`, or `"never"`.
`stop`	Union[None, str, List[str]]	None	List of strings that stop generation when produced. Output will not contain these strings.
`stop_token_ids`	Optional[List[int]]	None	List of token IDs that stop generation when produced. Output contains these tokens unless they are special tokens.
`ignore_eos`	bool	False	Whether to ignore the End-Of-Sequence token and continue generating tokens after its generation.
`max_tokens`	int	16	Maximum number of tokens to generate per output sequence.
`skip_special_tokens`	bool	True	Whether to skip special tokens in the output.
`spaces_between_special_tokens`	bool	True	Whether to add spaces between special tokens in the output.

README.md

vLLM 0.2.6 Endpoint | Serverless Worker

Table of Contents

Setting up the Serverless Worker

Option 1: Deploy Any Model Using Pre-Built Docker Image

Prerequisites

Environment Variables

Option 2: Build Docker Image with Model Inside

Prerequisites

Arguments:

Example: Building an image with OpenChat-3.5

(Optional) Including Huggingface Token

Compatible Models

Usage

Endpoint Model Inputs

Text Input Formats

1. prompt

2. messages

Sampling Parameters

1. `prompt`

2. `messages`