Booster, according to Merriam-Webster dictionary:

• an auxiliary device for increasing force, power, pressure, or effectiveness
• the first stage of a multistage rocket providing thrust for the launching and the initial part of the flight

Large Model Booster aims to be an simple and mighty LLM inference accelerator both for those who needs to scale GPTs within production environment or just experiment with models on its own.

Superpowers

• Built with performance and scaling in mind thanks Golang and C++
• No more problems with Python dependencies
• CPU-only inference if needed: any Intel or AMD x64, ARM64 and Apple Silicon
• GPUs supported as well: Nvidia CUDA, Apple Metal, even OpenCL cards
• Split really big models between a number of GPU (warp LLaMA 70B with 2x RTX 3090)
• Great performance on CPU only machines, fast as hell inference on monsters with beefy GPUs
• Both regular FP16/FP32 models and their quantised versions are supported - 4-bit really rocks!
• Popular LLM architectures already there: LLaMA, Mistral, Gemma, etc...
• Special bonus: SOTA Janus Sampling for code generation and non English languages

Motivation

Within first month of llama.go development I was literally shocked of how original ggml.cpp project made it very clear - there are no limits for talented people on bringing mind-blowing features and moving to AI future.

So I've decided to start a new project where best-in-class C++ / CUDA core will be embedded into mighty Golang server ready for robust and performant inference at large scale within real production environments.

V3 Roadmap - Summer'24

• Rebrand project again :) Collider => Booster
• Complete LLaMA v3 and v3.1 support
• OpenAI API Chat Completion compatible endpoints
• Ollama compatible endpoints
• Interactive mode for chatting from command line
• Update Janus Sampling for LLaMA-3
• ... and finally V3 release!

V3+ Roadmap - Fall'24

• Broader integration with Ollama ecosystem
• Smarter context expanding when reaching its limits
• Embedded web UI with no external dependencies
• Native Windows binaries
• Prebuilt binaries for all platforms
• Support LLaVA multi-modal models inference
• Better code test coverage
• Perplexity computation useful for benchmarking

How to build on Mac?

Booster was (and still) developed on Mac with Apple Silicon M1 processor, so it's really easy peasy:

make mac

How to compile for CUDA on Ubuntu?

Follow step 1 and step 2, then just make!

Ubuntu Step 1: Install C++ and Golang compilers, as well some developer libraries

sudo apt update -y && sudo apt upgrade -y && \
apt install -y git git-lfs make build-essential && \
wget https://golang.org/dl/go1.21.5.linux-amd64.tar.gz && \
tar -xf go1.21.5.linux-amd64.tar.gz -C /usr/local && \
rm go1.21.5.linux-amd64.tar.gz && \
echo 'export PATH="${PATH}:/usr/local/go/bin"' >> ~/.bashrc && source ~/.bashrc

Ubuntu Step 2: Install Nvidia drivers and CUDA Toolkit 12.2 with NVCC

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin && \
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub && \
sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" && \
sudo apt update -y && \
sudo apt install -y cuda-toolkit-12-2

Now you are ready to rock!

make cuda

How to Run?

You shold go through steps below:

1. Build the server from sources [ Mac inference as example ]

make clean && make mac

2. Download the model, like [ Hermes 2 Pro ] based on [ LLaMA-v3-8B ] quantized to GGUF Q4KM format:

wget https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf

3. Create configuration file and place it to the same directory [ see config.sample.yaml ]

id: mac
host: localhost
port: 8080
log: booster.log
deadline: 180

pods:

  gpu:
    model: hermes
    prompt: chat
    sampling: janus
    threads: 1
    gpus: [ 100 ]
    batch: 512

models:

  hermes:
    name: Hermes2 Pro 8B
    path: ~/models/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
    context: 8K
    predict: 1K

prompts:

  chat:
    locale: en_US
    prompt: "Today is {DATE}. You are virtual assistant. Please answer the question."
    system: "<|im_start|>system\n{PROMPT}<|im_end|>"
    user: "\n<|im_start|>user\n{USER}<|im_end|>"
    assistant: "\n<|im_start|>assistant\n{ASSISTANT}<|im_end|>"

samplings:

  janus:
    janus: 1
    depth: 200
    scale: 0.97
    hi: 0.99
    lo: 0.96

4. When all is done, start the server with debug enabled to be sure it working

Launch Booster in interactive mode to just chatting with the model:

./booster

Launch Booster as server to handle all API endpoints and show debug info:

./booster --server --debug

5. Now use Booster with Ollama / OpenAI API or POST JSON to native Async API http://localhost:8080/jobs

{
    "id": "5fb8ebd0-e0c9-4759-8f7d-35590f6c9fc6",
    "prompt": "Who are you?"
}

6. See results with native HTTP GET to native Async API http://localhost:8080/jobs/5fb8ebd0-e0c9-4759-8f7d-35590f6c9fc6

{
{
    "id": "5fb8ebd0-e0c9-4759-8f7d-35590f6c9f77",
    "output": "I'm a virtual assistant.",
    "prompt": "Who are you?",
    "status": "finished"
}
}

7. See instructions within booster.service file on how to create daemond service out of this API server.