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--- |
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viewer: false |
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tags: [uv-script, vllm, gpu, inference] |
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--- |
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# vLLM Inference Scripts |
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Ready-to-run UV scripts for GPU-accelerated inference using [vLLM](https://github.com/vllm-project/vllm). |
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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! |
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## 📋 Available Scripts |
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### classify-dataset.py |
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Batch text classification using BERT-style encoder models (e.g., BERT, RoBERTa, DeBERTa, ModernBERT) with vLLM's optimized inference engine. |
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**Note**: This script is specifically for encoder-only classification models, not generative LLMs. |
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**Features:** |
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- 🚀 High-throughput batch processing |
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- 🏷️ Automatic label mapping from model config |
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- 📊 Confidence scores for predictions |
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- 🤗 Direct integration with Hugging Face Hub |
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**Usage:** |
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```bash |
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# Local execution (requires GPU) |
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uv run classify-dataset.py \ |
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davanstrien/ModernBERT-base-is-new-arxiv-dataset \ |
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username/input-dataset \ |
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username/output-dataset \ |
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--inference-column text \ |
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--batch-size 10000 |
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``` |
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**HF Jobs execution:** |
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```bash |
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hfjobs run \ |
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--flavor l4x1 \ |
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--secret HF_TOKEN=$(python -c "from huggingface_hub import HfFolder; print(HfFolder.get_token())") \ |
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vllm/vllm-openai:latest \ |
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/bin/bash -c ' |
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uv run https://huggingface.co/datasets/uv-scripts/vllm/resolve/main/classify-dataset.py \ |
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davanstrien/ModernBERT-base-is-new-arxiv-dataset \ |
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username/input-dataset \ |
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username/output-dataset \ |
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--inference-column text \ |
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--batch-size 100000 |
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' \ |
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--project vllm-classify \ |
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--name my-classification-job |
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``` |
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## 🎯 Requirements |
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All scripts in this collection require: |
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- **NVIDIA GPU** with CUDA support |
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- **Python 3.10+** |
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- **UV package manager** ([install UV](https://docs.astral.sh/uv/getting-started/installation/)) |
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## 🚀 Performance Tips |
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### GPU Selection |
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- **L4 GPU** (`--flavor l4x1`): Best value for classification tasks |
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- **A10 GPU** (`--flavor a10`): Higher memory for larger models |
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- Adjust batch size based on GPU memory |
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### Batch Sizes |
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- **Local GPUs**: Start with 10,000 and adjust based on memory |
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- **HF Jobs**: Can use larger batches (50,000-100,000) with cloud GPUs |
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## 📚 About vLLM |
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vLLM is a high-throughput inference engine optimized for: |
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- Fast model serving with PagedAttention |
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- Efficient batch processing |
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- Support for various model architectures |
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- Seamless integration with Hugging Face models |
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## 🔧 Technical Details |
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### UV Script Benefits |
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- **Zero setup**: Dependencies install automatically on first run |
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- **Reproducible**: Locked dependencies ensure consistent behavior |
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- **Self-contained**: Everything needed is in the script file |
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- **Direct execution**: Run from local files or URLs |
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### Dependencies |
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Scripts use UV's inline metadata with custom package indexes for vLLM's optimized builds: |
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```python |
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# /// script |
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# requires-python = ">=3.10" |
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# dependencies = ["vllm", "datasets", "torch", ...] |
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# |
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# [[tool.uv.index]] |
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# url = "https://flashinfer.ai/whl/cu126/torch2.6" |
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# |
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# [[tool.uv.index]] |
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# url = "https://wheels.vllm.ai/nightly" |
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# /// |
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``` |
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### Docker Image |
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For HF Jobs, we use the official vLLM Docker image: `vllm/vllm-openai:latest` |
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This image includes: |
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- Pre-installed CUDA libraries |
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- vLLM and all dependencies |
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- UV package manager |
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- Optimized for GPU inference |
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## 📝 Contributing |
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Have a vLLM script to share? We welcome contributions that: |
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- Solve real inference problems |
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- Include clear documentation |
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- Follow UV script best practices |
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- Include HF Jobs examples |
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## 🔗 Resources |
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- [vLLM Documentation](https://docs.vllm.ai/) |
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- [UV Documentation](https://docs.astral.sh/uv/) |
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- [UV Scripts Organization](https://huggingface.co/uv-scripts) |
