davanstrien HF Staff commited on
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Improve README with UV context and clarify BERT-style models

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  1. README.md +20 -5
README.md CHANGED
@@ -5,13 +5,17 @@ tags: [uv-script, vllm, gpu, inference]
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  # vLLM Inference Scripts
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- Ready-to-run scripts for GPU-accelerated inference using [vLLM](https://github.com/vllm-project/vllm).
 
 
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  ## πŸ“‹ Available Scripts
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  ### classify-dataset.py
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- Batch text classification using BERT-style models with vLLM's optimized inference engine.
 
 
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  **Features:**
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  - πŸš€ High-throughput batch processing
@@ -53,7 +57,7 @@ hfjobs run \
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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** (auto-installed via script)
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  ## πŸš€ Performance Tips
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@@ -76,14 +80,25 @@ vLLM is a high-throughput inference engine optimized for:
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  ## πŸ”§ Technical Details
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  ### Dependencies
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- Scripts use vLLM's nightly builds and FlashInfer for optimal performance:
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  ```python
 
 
 
 
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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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  ### Docker Image
@@ -106,5 +121,5 @@ Have a vLLM script to share? We welcome contributions that:
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  ## πŸ”— Resources
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  - [vLLM Documentation](https://docs.vllm.ai/)
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- - [HF Jobs Guide](https://huggingface.co/docs/hub/spaces-gpu-jobs)
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  - [UV Scripts Organization](https://huggingface.co/uv-scripts)
 
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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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+
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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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  ## πŸ“‹ 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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+
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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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  - πŸš€ High-throughput batch processing
 
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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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  ## πŸš€ Performance Tips
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  ## πŸ”§ 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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+
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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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  ## πŸ”— 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)