its-hub: A Python library for inference-time scaling

its_hub is a Python library for inference-time scaling of LLMs, focusing on mathematical reasoning tasks.

📚 Documentation

For comprehensive documentation, including installation guides, tutorials, and API reference, visit:

https://ai-innovation.team/its_hub

Quick Start

from its_hub.utils import SAL_STEP_BY_STEP_SYSTEM_PROMPT
from its_hub.lms import OpenAICompatibleLanguageModel, StepGeneration
from its_hub.algorithms import ParticleFiltering
from its_hub.integration.reward_hub import LocalVllmProcessRewardModel

# Initialize language model (requires vLLM server)
lm = OpenAICompatibleLanguageModel(
    endpoint="http://localhost:8000/v1", 
    api_key="NO_API_KEY", 
    model_name="Qwen/Qwen2.5-Math-1.5B-Instruct", 
    system_prompt=SAL_STEP_BY_STEP_SYSTEM_PROMPT, 
)

# Set up inference-time scaling
sg = StepGeneration("\n\n", 32, r"\boxed")
prm = LocalVllmProcessRewardModel(
    model_name="Qwen/Qwen2.5-Math-PRM-7B", 
    device="cuda:0", 
    aggregation_method="prod"
)
scaling_alg = ParticleFiltering(sg, prm)

# Solve with inference-time scaling
result = scaling_alg.infer(lm, "Solve x^2 + 5x + 6 = 0", budget=8)

Installation

# Production
pip install its_hub

# Development
git clone https://github.com/Red-Hat-AI-Innovation-Team/its_hub.git
cd its_hub
pip install -e ".[dev]"

Key Features

• 🔬 Multiple Algorithms: Particle Filtering, Best-of-N, Beam Search, Self-Consistency
• 🚀 OpenAI-Compatible API: Easy integration with existing applications
• 🧮 Math-Optimized: Built for mathematical reasoning with specialized prompts
• 📊 Benchmarking Tools: Compare algorithms on MATH500 and AIME-2024 datasets
• ⚡ Async Support: Concurrent generation with limits and error handling

Development

git clone https://github.com/Red-Hat-AI-Innovation-Team/its_hub.git
cd its_hub
pip install -e ".[dev]"
pytest tests

For detailed documentation, visit: https://ai-innovation.team/its_hub