vLLM Inference Scripts

Ready-to-run UV scripts for GPU-accelerated inference using vLLM.

These scripts use UV's inline script metadata to automatically manage dependencies - just run with uv run and everything installs automatically!

📋 Available Scripts

classify-dataset.py

Batch text classification using BERT-style encoder models with vLLM's optimized inference engine.

Note: This script is specifically for encoder-only classification models, not generative LLMs.

Features:

• 🚀 High-throughput batch processing
• 🏷️ Automatic label mapping from model config
• 📊 Confidence scores for predictions
• 🤗 Direct integration with Hugging Face Hub

Usage:

# Local execution (requires GPU)
uv run classify-dataset.py \
    davanstrien/ModernBERT-base-is-new-arxiv-dataset \
    username/input-dataset \
    username/output-dataset \
    --inference-column text \
    --batch-size 10000

HF Jobs execution:

hfjobs run \
    --flavor l4x1 \
    --secret HF_TOKEN=$(python -c "from huggingface_hub import HfFolder; print(HfFolder.get_token())") \
    vllm/vllm-openai:latest \
    /bin/bash -c '
        uv run https://huggingface.co/datasets/uv-scripts/vllm/resolve/main/classify-dataset.py \
            davanstrien/ModernBERT-base-is-new-arxiv-dataset \
            username/input-dataset \
            username/output-dataset \
            --inference-column text \
            --batch-size 100000
    ' \
    --project vllm-classify \
    --name my-classification-job

🎯 Requirements

All scripts in this collection require:

• NVIDIA GPU with CUDA support
• Python 3.10+
• UV package manager (install UV)

🚀 Performance Tips

GPU Selection

• L4 GPU (--flavor l4x1): Best value for classification tasks
• A10 GPU (--flavor a10): Higher memory for larger models
• Adjust batch size based on GPU memory

Batch Sizes

• Local GPUs: Start with 10,000 and adjust based on memory
• HF Jobs: Can use larger batches (50,000-100,000) with cloud GPUs

📚 About vLLM

vLLM is a high-throughput inference engine optimized for:

• Fast model serving with PagedAttention
• Efficient batch processing
• Support for various model architectures
• Seamless integration with Hugging Face models

🔧 Technical Details

UV Script Benefits

• Zero setup: Dependencies install automatically on first run
• Reproducible: Locked dependencies ensure consistent behavior
• Self-contained: Everything needed is in the script file
• Direct execution: Run from local files or URLs

Dependencies

Scripts use UV's inline metadata with custom package indexes for vLLM's optimized builds:

# /// script
# requires-python = ">=3.10"
# dependencies = ["vllm", "datasets", "torch", ...]
# 
# [[tool.uv.index]]
# url = "https://flashinfer.ai/whl/cu126/torch2.6"
# 
# [[tool.uv.index]]
# url = "https://wheels.vllm.ai/nightly"
# ///

Docker Image

For HF Jobs, we use the official vLLM Docker image: vllm/vllm-openai:latest

This image includes:

• Pre-installed CUDA libraries
• vLLM and all dependencies
• UV package manager
• Optimized for GPU inference

📝 Contributing

Have a vLLM script to share? We welcome contributions that:

• Solve real inference problems
• Include clear documentation
• Follow UV script best practices
• Include HF Jobs examples

🔗 Resources

• vLLM Documentation
• UV Documentation