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What you can do with Evo VLM

EvoVLM is a research repo that uses LLM-guided evolutionary search to improve the inference programs for frozen Qwen3-VL-2B vision-language models on chart understanding.

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zzzz7788990213-ops/EvoVLM on GitHub

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What do you want to do?

4 use cases

  • Verify Evo VLM's recorded results on a CPU

    Replay the recorded evidence and run the final checks on a CPU, without model weights or new inference.

    View prompt
    Clone https://github.com/zzzz7788990213-ops/EvoVLM, enter research/evolving-vlm-inference-main, run `python3 -B -m evaluation.portable_reproduce` and then `bash reproduce.sh check`. Report the pass or fail status of each command and whether the recomputed Dev128 correct counts match the results table in the README. Do not run GPU inference and do not use `bash reproduce.sh` without a mode.

  • Smoke-test exported Evo VLM programs without weights

    Import the selected inference programs and check their interface on a CPU before any GPU setup.

    View prompt
    Clone https://github.com/zzzz7788990213-ops/EvoVLM and enter research/evolving-vlm-inference-main. Run `bash reproduce.sh smoke` and list each best_*.py file with whether it imports and passes the interface check. Verify that no model weights were loaded and no inference ran.

  • Run the evolved Qwen3-VL-2B programs on your charts

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

    View prompt
    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.

  • Adapt the Evo VLM evolution loop to a new VLM task

    Reuse the typed mutation catalog, evaluation protocols, and Pareto selection for a different vision-language task.

    View prompt
    Clone https://github.com/zzzz7788990213-ops/EvoVLM and read the evolution/ and evaluation/ folders in research/evolving-vlm-inference-main. Propose a plan to swap in <path to your task manifest> while keeping the typed mutation catalog and Pareto selection. Verify that every proposed mutation is listed in the existing catalog and does not add new code generation.

How to use Evo VLM

  1. Check you have what it needs: 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.
  2. Open zzzz7788990213-ops/EvoVLM on GitHub and read the README and license.
  3. git clone https://github.com/zzzz7788990213-ops/EvoVLM.git
  4. cd EvoVLM/research/evolving-vlm-inference-main
  5. python3 -B -m evaluation.portable_reproduce
  6. bash reproduce.sh check

These steps come from our top use case, Verify Evo VLM's recorded results on a CPU, which also has a copy-paste agent prompt.

Evo VLM alternatives · Best AI repos for Research

About this repo

  • Who it is forWritten for ML researchers and reviewers who want to audit recorded results or reuse the evolution method on chart understanding with frozen vision-language models.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/zzzz7788990213-ops/EvoVLM · Back to Hot repos

Requirements

  • 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
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Questions about Evo VLM

What is Evo VLM?

EvoVLM is a research repo that uses LLM-guided evolutionary search to improve the inference programs for frozen Qwen3-VL-2B vision-language models on chart understanding. It is an open-source project on GitHub: https://github.com/zzzz7788990213-ops/EvoVLM.

Who is Evo VLM for?

Written for ML researchers and reviewers who want to audit recorded results or reuse the evolution method on chart understanding with frozen vision-language models.

What can I do with Evo VLM?

4 use cases so far, for example: Verify Evo VLM's recorded results on a CPU; Smoke-test exported Evo VLM programs without weights; Run the evolved Qwen3-VL-2B programs on your charts. Each has steps and a copy-paste agent prompt.

What do I need to run Evo VLM?

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.

What are alternatives to Evo VLM?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Evo VLM 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.