vLLM 0.2.6 Endpoint | Serverless Worker
[](https://github.com/runpod-workers/worker-vllm/actions/workflows/docker-build-release.yml)
🚀 | 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
### 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:
```runpod/worker-vllm:dev```
#### 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`)
- `TRUST_REMOTE_CODE`: Whether to trust remote code with Hugging Face. (default: `0`)
- `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
[!WARNING] If you are getting errors while building the image, try adding `ENV MAX_JOBS` to the Dockerfile and increase Docker memory limit to at least 25GB.
To build an image with the model baked in, you must specify the following docker arguments when building the image:
#### Arguments:
- **Required**
- `MODEL_NAME`
- **Optional**
- `MODEL_BASE_PATH`: Defaults to `/runpod-volume` for network storage. Use `/models` or for local container storage.
- `QUANTIZATION`
- `HF_TOKEN`
- `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" .`
### 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.
## 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. |
### Messages Format
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.
Example:
```json
[
{
"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. |