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license: mit
---
This uv-script allows you to run batch inference on vllm over an hf dataset as long as it has a messages column. It's based on the script [https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py](https://huggingface.co/datasets/uv-scripts/vllm/raw/main/generate-responses.py)
the only diference is that it uses `llm.chat()` instead of `llm.generate()` so the response format is more familar to the openai response format and easier to use.
## Launch Job via SDK
```python
#!/usr/bin/env python3
from dotenv import load_dotenv
from huggingface_hub import HfApi
load_dotenv()
import os
DATASET_REPO_ID = "tytodd/test-job-dataset"
SCRIPT_URL = "https://huggingface.co/datasets/modaic/batch-vllm/raw/main/generate_responses.py"
def main() -> None:
api = HfApi()
job_info = api.run_uv_job(
SCRIPT_URL,
script_args=[
DATASET_REPO_ID,
DATASET_REPO_ID,
"--model-id",
# "Qwen/Qwen3-235B-A22B-Instruct-2507",
"deepseek-ai/DeepSeek-V3.2",
# "zai-org/GLM-5", # transformers > 5
# "moonshotai/Kimi-K2.5",
"--messages-column",
"messages",
],
dependencies=["transformers<5"],
image="vllm/vllm-openai:latest",
flavor="h200x4",
secrets={"HF_TOKEN": os.getenv("HF_TOKEN")},
)
print(f"Created job {job_info.id}")
print(job_info.url)
if __name__ == "__main__":
main()
```
## Launch Job via CLI
```
uvx hf jobs uv run \
--flavor l4x4 \
--secrets HF_TOKEN \
https://huggingface.co/datasets/modaic/batch-vllm/resolve/main/generate_responses.py \
username/input-dataset \
username/output-dataset \
--messages-column messages \
--model-id Qwen/Qwen3-30B-A3B-Instruct-2507 \
--temperature 0.7 \
--max-tokens 16384
``` |