---
viewer: false
tags: [uv-script, vllm, gpu, inference]
---

# vLLM Inference Scripts

Ready-to-run UV scripts for GPU-accelerated inference using [vLLM](https://github.com/vllm-project/vllm).

These scripts use [UV's inline script metadata](https://docs.astral.sh/uv/guides/scripts/) 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 (e.g., BERT, RoBERTa, DeBERTa, ModernBERT) 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:**
```bash
# 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:**
```bash
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](https://docs.astral.sh/uv/getting-started/installation/))

## 🚀 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:
```python
# /// 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](https://docs.vllm.ai/)
- [UV Documentation](https://docs.astral.sh/uv/)
- [UV Scripts Organization](https://huggingface.co/uv-scripts)