DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to evaluate the controller on vision-language tasks with State of Thought

Run the Qwen2.5-VL controller on A-OKVQA, AI2D, or M3CoT.

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

Credentials. Qwen2.5-VL weights may be gated on Hugging Face, so keep your hf auth login token out of shared logs and agent transcripts.

Who it is forA multimodal researcher who wants to test whether state-based evidence control transfers to image-plus-question reasoning.

The problemVision-language runs need extra dependencies and a different config than text runs, and mixing them up gives results that do not match the release.

The outcomeAn evaluation summary for a Qwen2.5-VL backbone on one of the three released vision-language benchmarks, with records written in the same format as the text runs.

What you need first

  • Python with pip and git installed
  • A virtual environment for the install
  • Hugging Face access for backbone weights, with license acceptance and hf auth login for gated models
  • Enough disk space and GPU memory for the chosen backbone when running evaluation

Steps

  1. Run python -m pip install -e ".[vlm]" inside the activated virtual environment (inferred, check the README)
  2. Run ls experiments/paper/ and pick the Qwen2.5-VL config file (inferred, check the README)
  3. Prepare the visual data as described in data/README.md
  4. Run sot data validate --config experiments/paper/<qwen2.5-vl config>.yaml (inferred, check the README)
  5. Run sot evaluate --config experiments/paper/<qwen2.5-vl config>.yaml (inferred, check the README)

Prompt for your agent

In the repo GongZhiren/State-of-Thought, install the package with the vlm extra, choose the Qwen2.5-VL config under experiments/paper/, then run sot data validate and sot evaluate on it. Report the benchmark, the backbone variant, and the evaluation summary path.

Caveat. The README lists Qwen2.5-VL-32B as BF16, which needs substantial GPU memory, and the vision-language extras are installed with a separate command the README describes only as adding .[vlm].

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page