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How to fine-tune a published EBFM checkpoint with SFT with Open EBFM

Start supervised fine-tuning from a Hugging Face EBFM model using the SFT launcher.

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

Who it is forAn ML practitioner who wants to adapt a published EBFM base model to a conversation or instruction dataset.

The problemAdapting a pretrained base model requires the right entry point, checkpoint reference, and dataset name.

The outcomeA supervised fine-tuning run that starts from the chosen Hugging Face model and writes outputs to the standard layout.

What you need first

  • Python with uv installed
  • A GPU environment (the README installs the gpu extra and loads checkpoints with device="cuda")
  • Network access for data preparation and Hugging Face downloads
  • Enough disk space for the datasets and checkpoints (sizes are not stated in the README)

Steps

  1. Run uv sync --extra gpu --extra hf --extra web and source .venv/bin/activate to set up the environment.
  2. Run bash runs/prepare_data.sh --datasets dclm or otherwise make sure your SFT data is available (the README says data is not included in the repository).
  3. Run TARGET_TOTAL_TOKENS=100000000 bash runs/sft.sh --dataset_name ebfm_sft --finetuning_model_ckpt guan-wang/EBFM-DCLM-1B to start fine-tuning. (inferred, check the README)
  4. Check outputs/checkpoints_hf/ after the run for the exported fine-tuned model.

Prompt for your agent

In the datamllab/OpenEBFM repo, run runs/sft.sh with --finetuning_model_ckpt set to <hf_model_id> and --dataset_name set to <dataset_name>. Report the output directory and confirm checkpoints_hf/ was created.

Caveat. The README names ebfm_sft as an example dataset name but does not describe how to prepare that dataset, so the data setup step may need extra work.

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