{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "yzmblog-dmad--retrain-dmad-student-on-released-data",
 "repo": "Yzmblog/DMAD",
 "repo_url": "https://github.com/Yzmblog/DMAD",
 "repo_slug": "yzmblog-dmad",
 "title": "Retrain a DMAD student from the released training code",
 "page_title": "How to retrain a DMAD student from the released training code with DMAD",
 "summary": "Use the train folder and the published Wan2.1 training data to start a DMAD training run.",
 "who": "A researcher or ML engineer who wants to reproduce or extend DMAD training on Wan2.1 data.",
 "problem": "Reproducing a distillation result needs the training code, the data, and the environment set up together.",
 "outcome": "A training run that starts from the published Wan2.1 data with the code in train/wan.",
 "steps": [
  "Read train/wan/README.md, including its data section, and follow its environment setup.",
  "Download the data with `hf download ZhengmingYu/DMAD-Wan-data --repo-type dataset --local-dir data/wan`. (inferred, check the README)",
  "Point the training config at the downloaded data folder as described in train/wan/README.md.",
  "Start training with the command given in train/wan/README.md and check the first logged steps."
 ],
 "agent_prompt": "In the Yzmblog/DMAD repo, read train/wan/README.md, download the DMAD-Wan-data dataset to data/wan, and start the training command from that README. Expected output: training logs for the Wan2.1 DMAD student. Verify that the dataset folder contains the 42,158 teacher samples the README lists before starting training.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "4 hours",
 "basis": "readme",
 "caveat": "The download command is inferred from the Hugging Face dataset link, so confirm the exact command in train/wan/README.md before running it.",
 "category": "Content",
 "tags": [
  "Content"
 ],
 "lang": "python",
 "requirements": [
  "A CUDA GPU: 80 GB for the default path, 8 GB and up with --low-vram",
  "Python 3.12 with conda and PyTorch for CUDA 12.6",
  "About 170 GB of free disk space for the MiniMax-H3 base components",
  "A Hugging Face account that has accepted the MiniMax H3 Community License"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/yzmblog-dmad/retrain-dmad-student-on-released-data/",
 "md": "https://dedupedai.com/use-cases/yzmblog-dmad/retrain-dmad-student-on-released-data/index.md",
 "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 hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/yzmblog-dmad--retrain-dmad-student-on-released-data.json",
 "api_version": "v1"
}