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How to serve a looped model behind an OpenAI-style endpoint with vLLM RLT

Host Ouro-1.4B or another supported checkpoint so apps can call a streaming completions API.

Who it is forBackend engineer who needs a self-hosted text generation endpoint for an internal app and has an NVIDIA GPU host.

The problemCalling a recurrent language model directly from app code is hard to batch and stream, and the team wants a familiar completions interface.

The outcomeA running server that answers completions requests with streamed tokens, reachable from any client that speaks the OpenAI completions format.

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 after activating your Python environment.
  2. Follow the step-by-step user guide in docs/launching.md to create an environment and download the chosen checkpoint.
  3. Start the server as described in docs/launching.md.
  4. Send a first completions request to the server and confirm that tokens come back.
  5. Point your app at the endpoint and enable streaming if the app needs incremental output.

Prompt for your agent

In the vllm-rlt repository, follow docs/launching.md to install the project, download ByteDance/Ouro-1.4B, and start the server. Send one completions request with the prompt <your test prompt> and report the returned text. Verify that the response is non-empty and that the server log shows no errors.

Caveat. The README calls v0.1.0 experimental and recommends starting with synchronous execution, so keep advanced async options off for the first deployment.

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