metadata

viewer: false
tags:
  - uv-script
  - vllm
  - gpu
  - inference

vLLM Inference Scripts

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

📋 Available Scripts

classify-dataset.py

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

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 (auto-installed via script)

🚀 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

Dependencies

Scripts use vLLM's nightly builds and FlashInfer for optimal performance:

# [[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

Datasets:

uv-scripts
/

vllm