secret

README.md

@@ -18,12 +18,14 @@ Batch text classification using BERT-style encoder models (e.g., BERT, RoBERTa,
 **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 \
@@ -35,6 +37,7 @@ uv run classify-dataset.py \
 ```
 
 **HF Jobs execution:**
+
 ```bash
 hf jobs uv run \
     --flavor l4x1 \
@@ -52,6 +55,7 @@ hf jobs uv run \
 Generate responses for chat-formatted prompts using generative LLMs (e.g., Llama, Qwen, Mistral) with vLLM's high-performance inference engine.
 
 **Features:**
+
 - 💬 Automatic chat template application
 - 🔀 Multi-GPU tensor parallelism support
 - 📏 Smart filtering for prompts exceeding context length
@@ -60,6 +64,7 @@ Generate responses for chat-formatted prompts using generative LLMs (e.g., Llama
 - 🎛️ Full control over sampling parameters
 
 **Usage:**
+
 ```bash
 # Local execution with default Qwen model
 uv run generate-responses.py \
@@ -79,12 +84,13 @@ uv run generate-responses.py \
 ```
 
 **HF Jobs execution (multi-GPU):**
+
 ```bash
 hf jobs uv run \
     --flavor l4x4 \
     --image vllm/vllm-openai \
     -e UV_PRERELEASE=if-necessary \
-    -e HF_TOKEN=hf_*** \
+    -s HF_TOKEN=hf_*** \
     https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \
     davanstrien/cards_with_prompts \
     davanstrien/test-generated-responses \
@@ -97,6 +103,7 @@ hf jobs uv run \
 ## 🎯 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/))
@@ -104,6 +111,7 @@ All scripts in this collection require:
 ## 🚀 Performance Tips
 
 ### GPU Selection
+
 - **L4 GPU** (`--flavor l4x1`): Best value for classification and smaller models
 - **L4x4** (`--flavor l4x4`): Multi-GPU setup for large models (30B+ parameters)
 - **A10 GPU** (`--flavor a10g-large`): Higher memory for larger models
@@ -111,16 +119,20 @@ All scripts in this collection require:
 - Adjust batch size and tensor parallelism based on GPU configuration
 
 ### Batch Sizes
+
 - **Classification**: Start with 10,000 locally, up to 100,000 on HF Jobs
 - **Generation**: vLLM handles batching automatically - no manual configuration needed
 
 ### Multi-GPU Tensor Parallelism
+
 - Auto-detects available GPUs by default
 - Use `--tensor-parallel-size` to manually specify
 - Required for models larger than single GPU memory (e.g., 30B+ models)
 
 ### Handling Long Contexts
+
 The generate-responses.py script includes smart prompt filtering:
+
 - **Default behavior**: Skips prompts exceeding max_model_len
 - **Use `--max-model-len`**: Limit context to reduce memory usage
 - **Use `--no-skip-long-prompts`**: Fail on long prompts instead of skipping
@@ -129,6 +141,7 @@ The generate-responses.py script includes smart prompt filtering:
 ## 📚 About vLLM
 
 vLLM is a high-throughput inference engine optimized for:
+
 - Fast model serving with PagedAttention
 - Efficient batch processing
 - Support for various model architectures
@@ -137,13 +150,16 @@ vLLM is a high-throughput inference engine optimized for:
 ## 🔧 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 for automatic dependency management:
+
 ```python
 # /// script
 # requires-python = ">=3.10"
@@ -161,15 +177,18 @@ Scripts use UV's inline metadata for automatic dependency management:
 For bleeding-edge features, use the `UV_PRERELEASE=if-necessary` environment variable to allow pre-release versions when needed.
 
 ### Docker Image
+
 For HF Jobs, we recommend the official vLLM Docker image: `vllm/vllm-openai`
 
 This image includes:
+
 - Pre-installed CUDA libraries
 - vLLM and all dependencies
 - UV package manager
 - Optimized for GPU inference
 
 ### Environment Variables
+
 - `HF_TOKEN`: Your Hugging Face authentication token (auto-detected if logged in)
 - `UV_PRERELEASE=if-necessary`: Allow pre-release packages when required
 - `HF_HUB_ENABLE_HF_TRANSFER=1`: Automatically enabled for faster downloads
@@ -177,6 +196,7 @@ This image includes:
 ## 📝 Contributing
 
 Have a vLLM script to share? We welcome contributions that:
+
 - Solve real inference problems
 - Include clear documentation
 - Follow UV script best practices
@@ -186,4 +206,4 @@ Have a vLLM script to share? We welcome contributions that:
 
 - [vLLM Documentation](https://docs.vllm.ai/)
 - [UV Documentation](https://docs.astral.sh/uv/)
-- [UV Scripts Organization](https://huggingface.co/uv-scripts)
+- [UV Scripts Organization](https://huggingface.co/uv-scripts)