{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "omnijev-onejev--fine-tune-onejev-on-own-questions",
 "repo": "OmniJev/OneJev",
 "repo_url": "https://github.com/OmniJev/OneJev",
 "repo_slug": "omnijev-onejev",
 "title": "Fine-tune One Jev on your own labeled questions",
 "page_title": "How to fine-tune One Jev on your own labeled questions with One Jev",
 "summary": "Train a checkpoint on your own media and typed questions using the repo's config files.",
 "who": "A machine learning engineer who has labeled screenshots or media for a narrow decision task and needs a model tuned to it.",
 "problem": "The released models may not match the questions, answer options or media in your own product.",
 "outcome": "A fine-tuned checkpoint saved under train/runs that you can serve with qev serve --model.",
 "steps": [
  "Run `git clone https://github.com/OmniJev/OneJev.git`, then `cd OneJev` and `pip install -e \".[train]\"`",
  "Run `hf download OmniJev/OneJev-Data sample/sample-100.parquet --repo-type dataset --local-dir data/onejev` and `python -m train.unpack data/onejev --sample` to try the 100-example demo",
  "Write your own train.jsonl and media folder in the same layout, then set `train` and `media_root` in a copy of train/configs/onejev_4b_full.yaml (inferred, check the README)",
  "Run `torchrun --nproc-per-node 4 -m train.sft --config train/configs/onejev_4b_full.yaml`, changing the GPU count to match your machine",
  "Run `qev serve --model train/runs/onejev_4b/final --port 8000` to test the checkpoint"
 ],
 "agent_prompt": "Using the OneJev repo at https://github.com/OmniJev/OneJev, install with `pip install -e \".[train]\"` and run the 100-example demo unpack step. Report the path to train/configs/onejev_4b_full.yaml and the number of lines in data/onejev/train.jsonl. Verify that data/onejev/media/ exists before you start training.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The 4B config targets four GPUs and the full dataset is 17.7 GB, so the 100-example sample is the only practical start on a single machine.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Python with pip",
  "An NVIDIA GPU for the PyTorch backend, or llama.cpp installed for the GGUF backend",
  "Docker, only if you use the Docker option",
  "A Hugging Face token only for gated or private repos (set HF_TOKEN in docker/.env for Docker)"
 ],
 "risk": "dual-use",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.7,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/omnijev-onejev/fine-tune-onejev-on-own-questions/",
 "md": "https://dedupedai.com/use-cases/omnijev-onejev/fine-tune-onejev-on-own-questions/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/omnijev-onejev--fine-tune-onejev-on-own-questions.json",
 "api_version": "v1"
}