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How to generate text for a prompt list offline in Python with vLLM RLT

Run many prompts through a supported checkpoint in one Python script without starting a server.

Who it is forData engineer who needs to generate text for a fixed list of prompts as part of a batch job on a GPU host.

The problemA batch job should not depend on a running HTTP server, but it still needs efficient batching across prompts.

The outcomeA script that writes one generated output per input prompt to a file, using the offline Python API.

What you need first

  • Linux machine with an NVIDIA GPU and a CUDA-enabled PyTorch installation compatible with the hardware
  • Python 3.10 or newer and PyTorch 2.5 or newer
  • uv installed to run the install command from the repository root
  • Access to download the chosen checkpoint, such as ByteDance/Ouro-1.4B

Steps

  1. Run uv pip install -e . from the repository root.
  2. Follow docs/launching.md to download the chosen checkpoint, then read the Python API section of the documentation.
  3. Write a Python script that loads the checkpoint through the Python API and passes your prompt list as a batch. (inferred, check the README)
  4. Choose greedy decoding for repeatable output or seeded top-k or top-p sampling for varied output.
  5. Write each prompt and its generated text to an output file and check the line count against the input.

Prompt for your agent

In the vllm-rlt repository, write and run a Python script that loads ByteDance/Ouro-1.4B through the Python API and generates text for every prompt in <input file>, writing results to <output file>. Expected output is one line per prompt. Verify that the output has the same number of lines as the input.

Caveat. The README documents the Python API and sampling options at a feature level, so confirm exact function names in the documentation before relying on them in a script.

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