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What you can do with vLLM RLT

vllm-rlt is a standalone inference and serving engine for recurrent language models such as Ouro-1.4B, Nanbeige4.2-3B, and Huginn-0125.

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What do you want to do?

4 use cases

  • Serve a looped model behind an OpenAI-style endpoint

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

    View prompt
    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.

  • Compare fixed-depth and early-exit decoding on Ouro

    Measure how loop depth settings change Ouro-1.4B outputs for a chosen set of prompts.

    View prompt
    In the vllm-rlt repository, use the Python API to run Ouro-1.4B on the prompts in <prompt file> twice, once with fixed four-loop decoding and once with the early-exit gate. Output a table with prompt, both answers, and the loop depth used. Verify that both runs use the same prompts and the same decoding settings except for the exit policy.

  • Generate text for a prompt list offline in Python

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

    View prompt
    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.

  • Benchmark depth-aware serving on your own GPUs

    Reproduce the published baseline protocol on your hardware before choosing a serving layout.

    View prompt
    In the vllm-rlt repository, follow the protocol in the linked PR #30 and PR #31 to benchmark Ouro-1.4B BF16 on <your GPU setup> with a single replica and with a split prefill/decode layout. Output a table of throughput, TTFT, and ITL for each layout. Verify that the workload settings match across both runs.

How to use vLLM RLT

  1. Check you have what it needs: 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.
  2. Open InferMatrixLab/vllm-rlt on GitHub and read the README and license.
  3. Run uv pip install -e . from the repository root after activating your Python environment.
  4. Follow the step-by-step user guide in docs/launching.md to create an environment and download the chosen checkpoint.
  5. Start the server as described in docs/launching.md.
  6. Send a first completions request to the server and confirm that tokens come back.

These steps come from our top use case, Serve a looped model behind an OpenAI-style endpoint, which also has a copy-paste agent prompt.

vLLM RLT alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forBuilt for ML engineers and infrastructure teams who run recurrent or looped language models on NVIDIA GPUs and want batching, serving, and benchmarking.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/InferMatrixLab/vllm-rlt · Back to Hot repos

Requirements

  • 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
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Questions about vLLM RLT

What is vLLM RLT?

vllm-rlt is a standalone inference and serving engine for recurrent language models such as Ouro-1.4B, Nanbeige4.2-3B, and Huginn-0125. It is an open-source project on GitHub: https://github.com/InferMatrixLab/vllm-rlt.

Who is vLLM RLT for?

Built for ML engineers and infrastructure teams who run recurrent or looped language models on NVIDIA GPUs and want batching, serving, and benchmarking.

What can I do with vLLM RLT?

4 use cases so far, for example: Serve a looped model behind an OpenAI-style endpoint; Compare fixed-depth and early-exit decoding on Ouro; Generate text for a prompt list offline in Python. Each has steps and a copy-paste agent prompt.

What do I need to run vLLM RLT?

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.

What are alternatives to vLLM RLT?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is vLLM RLT safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.