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---
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) |