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How to run the evolved Qwen3-VL-2B programs on your charts with Evo VLM

Set up the recorded GPU environment and model snapshots to run the selected programs on your own chart questions.

  • Research
  • Advanced
  • 3 hours
  • From the README
  • Any agent

Who it is forAn ML engineer with a GPU who wants to test whether the evolved Instruct or Thinking program helps on chart questions in their own data.

The problemThe published results cover one benchmark subset, so the engineer needs to see how the selected programs behave on their own charts and latency budget.

The outcomeThe engineer runs the selected programs with pinned Qwen3-VL-2B snapshots and records accuracy and latency on their own question set.

What you need first

  • Git and Python 3 installed locally
  • A clone of https://github.com/zzzz7788990213-ops/EvoVLM
  • For GPU inference only: a CUDA-capable GPU, PyTorch, Transformers, and the external Qwen model snapshots described in the runtime guide

Steps

  1. Clone the repo and enter research/evolving-vlm-inference-main as in the quick start.
  2. Read requirements.lock.txt in that folder and note that it lists Linux and CUDA-specific packages.
  3. Follow the runtime guide README.md in that folder for pinned model downloads, local overrides, and offline behavior.
  4. Run the example inference commands given in that runtime guide against your own chart questions.
  5. Compare the output against the manual seed programs and the baseline on the same questions.

Prompt for your agent

In the EvoVLM repo at research/evolving-vlm-inference-main, follow the runtime guide README.md to set up pinned Qwen3-VL-2B snapshots and run the Evolved Instruct program on <path to your chart question file>. Report the answers, median latency, and accuracy against your labels. Verify that the environment matches requirements.lock.txt before comparing numbers.

Caveat. Model weights are not included in the repo and latency depends on the recorded hardware and software environment, so results on your setup may differ from the published Speed32 numbers.

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