Rubra-vLLM

This is Rubra's fork of vLLM, offering easy, fast, and cheap LLM serving for everyone of Rubra's function calling models (and others)

Rubra-vLLM Quickstart

1. Install

# export cuda path
export CUDA_HOME=/usr/local/cuda
export PATH="${CUDA_HOME}/bin:$PATH"

# install from source
pip install -e . # this could take 5-10 minutes
# install a helper package using npm, this handles some rare edgecases parsing function calls.
npm install jsonrepair

2. Start the Server with a Rubra function calling model:

python -m vllm.entrypoints.openai.api_server --model rubra-ai/Phi-3-mini-128k-instruct-function-calling-alpha-v1  --dtype auto --api-key token-abc123 --max-model-len 8000 --gpu-memory-utilization 0.96 --enforce-eager

The model will get downloaded automatically from huggingface.

3. Test the server, make sure it is available:

curl localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer token-abc123" \
  -d '{
    "model": "rubra-ai/Phi-3-mini-128k-instruct-function-calling-alpha-v1",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "hello"
      }
    ]
  }'

4. Try a python function calling example:

# if openai not installed, do `pip install openai`
from openai import OpenAI
client = OpenAI(api_key="token-abc123", base_url = "http://localhost:8000/v1/")

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
          },
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
        },
        "required": ["location"],
      },
    }
  }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
completion = client.chat.completions.create(
  model="rubra-ai/Phi-3-mini-128k-instruct-function-calling-alpha-v1",
  messages=messages,
  tools=tools,
  tool_choice="auto"
)

print(completion)

The output should look like this:

ChatCompletion(id='chatcmpl-EmHd8kai4DVwBUOyim054GmfcyUbjiLf', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='e885974b', function=Function(arguments='{"location":"Boston"}', name='get_current_weather'), type='function')]))], created=1719528056, model='rubra-model', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=29, prompt_tokens=241, total_tokens=270))

That's it! MAKE SURE you turn stream OFF when making api calls to the server, as the streaming feature is not supported yet. And we will support streaming too soon.

About

vLLM is a fast and easy-to-use library for LLM inference and serving.

vLLM is fast with:

• State-of-the-art serving throughput
• Efficient management of attention key and value memory with PagedAttention
• Continuous batching of incoming requests
• Fast model execution with CUDA/HIP graph
• Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
• Optimized CUDA kernels

vLLM is flexible and easy to use with:

• Seamless integration with popular Hugging Face models
• High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
• Tensor parallelism support for distributed inference
• Streaming outputs
• OpenAI-compatible API server
• Support NVIDIA GPUs, AMD GPUs, Intel CPUs and GPUs
• (Experimental) Prefix caching support
• (Experimental) Multi-lora support

vLLM seamlessly supports most popular open-source models on HuggingFace, including:

• Transformer-like LLMs (e.g., Llama)
• Mixture-of-Expert LLMs (e.g., Mixtral)
• Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported models here.

Getting Started

Install vLLM with pip or from source:

pip install vllm

Visit our documentation to learn more.

• Installation
• Quickstart
• Supported Models

Contributing

We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.

Sponsors

vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!

• a16z
• AMD
• Anyscale
• AWS
• Crusoe Cloud
• Databricks
• DeepInfra
• Dropbox
• Lambda Lab
• NVIDIA
• Replicate
• Roblox
• RunPod
• Sequoia Capital
• Trainy
• UC Berkeley
• UC San Diego
• ZhenFund

We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.

Citation

If you use vLLM for your research, please cite our paper:

@inproceedings{kwon2023efficient,
  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
  year={2023}
}