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How to fine-tune One Jev on your own labeled questions with One Jev

Train a checkpoint on your own media and typed questions using the repo's config files.

Authorization notice. One Jev is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA machine learning engineer who has labeled screenshots or media for a narrow decision task and needs a model tuned to it.

The problemThe released models may not match the questions, answer options or media in your own product.

The outcomeA fine-tuned checkpoint saved under train/runs that you can serve with qev serve --model.

What you need first

  • 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)

Steps

  1. Run git clone https://github.com/OmniJev/OneJev.git, then cd OneJev and pip install -e ".[train]"
  2. 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
  3. 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)
  4. Run torchrun --nproc-per-node 4 -m train.sft --config train/configs/onejev_4b_full.yaml, changing the GPU count to match your machine
  5. Run qev serve --model train/runs/onejev_4b/final --port 8000 to test the checkpoint

Prompt for your agent

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.

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.

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