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README.md

@@ -367,7 +367,7 @@ Two scripts do this. `classify-dataset.py` runs on vLLM and is the one to start
 A GPU is required. Run on Jobs with the vLLM image:
 
 ```bash
-hf jobs uv run --flavor l4x1 --image vllm/vllm-openai:latest --secrets HF_TOKEN \
+hf jobs uv run --flavor l4x1 --image vllm/vllm-openai:v0.32.0 --secrets HF_TOKEN \
   https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
   --input-dataset stanfordnlp/imdb \
   --column text \
@@ -382,7 +382,7 @@ date or label. Drop both to label the whole split.
 ### With reasoning and label descriptions
 
 ```bash
-hf jobs uv run --flavor l4x1 --image vllm/vllm-openai:latest --secrets HF_TOKEN \
+hf jobs uv run --flavor l4x1 --image vllm/vllm-openai:v0.32.0 --secrets HF_TOKEN \
   https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \
   --input-dataset user/support-tickets \
   --column content \
@@ -408,7 +408,7 @@ model must end with `{"label": "..."}`, and rows where that cannot be parsed are
 | `--max-samples` | Label only the first N rows (or N random rows with `--shuffle`) |
 | `--shuffle`, `--shuffle-seed` | Shuffle before `--max-samples` (seed default 42) |
 
-Run `uv run classify-dataset.py --help` for all options.
+Run `uv run classify-dataset.py --help` for all options (no GPU or vLLM needed).
 
 ### Good to know
 
@@ -418,5 +418,7 @@ Run `uv run classify-dataset.py --help` for all options.
   truncated to 4,000 characters.
 - **Label names matter.** Use clear, distinct names, add `--label-descriptions` when names are
   ambiguous, and try a larger model for nuanced tasks.
-- **vLLM version**: `ImportError: cannot import name GuidedDecodingParams` means the vLLM
-  version does not match; the script requires `vllm>=0.6.6`.
+- **vLLM version**: on Jobs, vLLM, PyTorch and Transformers come from the `vllm/vllm-openai`
+  image, so the script header does not list them. For a local GPU run (Linux + CUDA), add vLLM
+  yourself: `uv run --with vllm classify-dataset.py ...`. The script needs vLLM 0.12 or newer
+  (`StructuredOutputsParams`).

classify-dataset.py

@@ -2,14 +2,16 @@
 # /// script
 # requires-python = ">=3.10"
 # dependencies = [
-#     "vllm>=0.12.0",  # StructuredOutputsParams API (guided_decoding removed in 0.12.0)
-#     "transformers>=4.53.0",
-#     "torch",
 #     "datasets",
 #     "huggingface-hub[hf_transfer]",
 # ]
 #
 # [tool.hf-jobs]
+# # vllm, torch and transformers come from the image (PYTHONPATH). Listing them above makes uv
+# # install newer PyPI copies plus their deps. PYTHONPATH shadows the copies, but the current
+# # flashinfer-python wheel (0.7.2.post1) also ships a top-level b12x module that the image lacks.
+# # The image's vllm imports it and crashes (missing flashinfer/experimental/b12x/_api.py).
+# # Seen on v0.32.0 (2026-11-05); re-test before moving the tag. Local GPU: uv run --with vllm.
 # image = "vllm/vllm-openai:v0.32.0"
 # python = "/usr/bin/python3"
 # env = { PYTHONPATH = "/usr/local/lib/python3.12/dist-packages" }
@@ -24,8 +26,9 @@ Classify text columns in Hugging Face datasets using vLLM with structured output
 This script provides efficient GPU-based classification with guaranteed valid outputs,
 optimized for running on HF Jobs.
 
-Example:
-    uv run classify-dataset.py \\
+Example (local GPU, Linux + CUDA; the header leaves vllm, torch and transformers to
+the HF Jobs image, so add vllm with --with; the script needs vLLM 0.12 or newer):
+    uv run --with vllm classify-dataset.py \\
         --input-dataset imdb \\
         --column text \\
         --labels "positive,negative" \\
@@ -44,14 +47,17 @@ import argparse
 import logging
 import os
 import sys
-from typing import List
+from typing import TYPE_CHECKING, List
 
-import torch
 from datasets import load_dataset
 from huggingface_hub import HfApi, get_token
-from transformers import AutoTokenizer
-from vllm import LLM, SamplingParams
-from vllm.sampling_params import StructuredOutputsParams
+
+if TYPE_CHECKING:
+    from transformers import AutoTokenizer
+
+# torch, transformers and vllm are imported inside main(), after argument parsing, so
+# `uv run classify-dataset.py --help` works without them. On HF Jobs they come from the
+# vLLM image (see the header).
 
 # Default model - SmolLM3 for good balance of speed and quality
 DEFAULT_MODEL = "HuggingFaceTB/SmolLM3-3B"
@@ -337,7 +343,7 @@ def validate_text(text: str) -> bool:
 
 
 def prepare_prompts(
-    texts: List[str], labels: List[str], tokenizer: AutoTokenizer, 
+    texts: List[str], labels: List[str], tokenizer: "AutoTokenizer", 
     label_descriptions: dict = None, enable_reasoning: bool = False
 ) -> tuple[List[str], List[int]]:
     """Prepare prompts using chat template for classification, filtering invalid texts."""
@@ -365,6 +371,11 @@ def prepare_prompts(
 def main():
     args = parse_args()
 
+    import torch
+    from transformers import AutoTokenizer
+    from vllm import LLM, SamplingParams
+    from vllm.sampling_params import StructuredOutputsParams
+
     # Check authentication early
     logger.info("Checking authentication...")
     token = args.hf_token or (os.environ.get("HF_TOKEN") or get_token())
@@ -601,22 +612,22 @@ def main():
 
 if __name__ == "__main__":
     if len(sys.argv) == 1:
-        print("Example commands:")
+        print("Example commands (local GPU runs add vLLM with --with; on HF Jobs the image provides it):")
         print("\n# Simple classification:")
-        print("uv run classify-dataset.py \\")
+        print("uv run --with vllm classify-dataset.py \\")
         print("  --input-dataset stanfordnlp/imdb \\")
         print("  --column text \\")
         print("  --labels 'positive,negative' \\")
         print("  --output-dataset user/imdb-classified")
         print("\n# With label descriptions:")
-        print("uv run classify-dataset.py \\")
+        print("uv run --with vllm classify-dataset.py \\")
         print("  --input-dataset user/support-tickets \\")
         print("  --column content \\")
         print("  --labels 'bug,feature,question' \\")
         print("  --label-descriptions 'bug:something is broken or not working,feature:request for new functionality,question:asking for help or clarification' \\")
         print("  --output-dataset user/tickets-classified")
         print("\n# With reasoning mode (thinking + JSON output):")
-        print("uv run classify-dataset.py \\")
+        print("uv run --with vllm classify-dataset.py \\")
         print("  --input-dataset stanfordnlp/imdb \\")
         print("  --column text \\")
         print("  --labels 'positive,negative,neutral' \\")