Update README.md with enhanced usage instructions for classify-dataset.py and generate-responses.py, including multi-GPU support and environment variable details.

README.md

@@ -36,20 +36,62 @@ uv run classify-dataset.py \
 
 **HF Jobs execution:**
 ```bash
-hfjobs run \
+hf jobs uv 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
+    --image vllm/vllm-openai \
+    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
+```
+
+### generate-responses.py
+
+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
+- 📊 Comprehensive dataset cards with generation metadata
+- ⚡ HF Transfer enabled for fast model downloads
+- 🎛️ Full control over sampling parameters
+
+**Usage:**
+```bash
+# Local execution with default Qwen model
+uv run generate-responses.py \
+    username/input-dataset \
+    username/output-dataset \
+    --messages-column messages \
+    --max-tokens 1024
+
+# With custom model and parameters
+uv run generate-responses.py \
+    username/input-dataset \
+    username/output-dataset \
+    --model-id meta-llama/Llama-3.1-8B-Instruct \
+    --temperature 0.9 \
+    --top-p 0.95 \
+    --max-model-len 8192
+```
+
+**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_*** \
+    https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py \
+    davanstrien/cards_with_prompts \
+    davanstrien/test-generated-responses \
+    --model-id Qwen/Qwen3-30B-A3B-Instruct-2507 \
+    --gpu-memory-utilization 0.9 \
+    --max-tokens 600 \
+    --max-model-len 8000
 ```
 
 ## 🎯 Requirements
@@ -62,13 +104,27 @@ All scripts in this collection require:
 ## 🚀 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
+- **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
+- **A100** (`--flavor a100-large`): Maximum performance for demanding workloads
+- Adjust batch size and tensor parallelism based on GPU configuration
 
 ### 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
+- **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
+- Skipped prompts receive empty responses and are logged
 
 ## 📚 About vLLM
 
@@ -87,22 +143,25 @@ vLLM is a high-throughput inference engine optimized for:
 - **Direct execution**: Run from local files or URLs
 
 ### Dependencies
-Scripts use UV's inline metadata with custom package indexes for vLLM's optimized builds:
+Scripts use UV's inline metadata for automatic dependency management:
 ```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"
+# dependencies = [
+#     "datasets",
+#     "flashinfer-python",
+#     "huggingface-hub[hf_transfer]",
+#     "torch",
+#     "transformers",
+#     "vllm",
+# ]
 # ///
 ```
 
+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 use the official vLLM Docker image: `vllm/vllm-openai:latest`
+For HF Jobs, we recommend the official vLLM Docker image: `vllm/vllm-openai`
 
 This image includes:
 - Pre-installed CUDA libraries
@@ -110,6 +169,11 @@ This image includes:
 - 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
+
 ## 📝 Contributing
 
 Have a vLLM script to share? We welcome contributions that: