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How to measure BPB and QA scores for an EBFM checkpoint with Open EBFM

Run position-wise BPB and QA evaluation on a published model and collect results.

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

Who it is forA researcher comparing EBFM checkpoints who needs consistent benchmark numbers.

The problemComparing language models needs the same metrics computed the same way across checkpoints.

The outcomePosition-wise BPB and QA result files written under results/ for the chosen checkpoint.

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. Download the local snapshot with MODEL_DIR="$(python -c 'from huggingface_hub import snapshot_download; print(snapshot_download(repo_id="guan-wang/EBFM-DCLM-1B"))')" so the BPB metric has token_bytes.pt.
  3. Run bash runs/eval.sh --checkpoint guan-wang/EBFM-DCLM-1B --dataset dclm --data-dir data/dclm --tokenizer-path "${MODEL_DIR}" --output_root . --run_name eval-dclm for BPB. (inferred, check the README)
  4. Run bash runs/qa.sh --checkpoint guan-wang/EBFM-DCLM-1B --tokenizer-path "${MODEL_DIR}" --eval-bundle data/eval_bundle --output_root . --run_name qa-dclm for QA. (inferred, check the README)

Prompt for your agent

In the datamllab/OpenEBFM repo, run runs/eval.sh for BPB and runs/qa.sh for QA on the checkpoint <hf_model_id>, using the local snapshot directory for the tokenizer path. Report the results/ paths and confirm token_bytes.pt was found in the snapshot.

Caveat. BPB evaluation needs the local snapshot directory, and the eval bundle at data/eval_bundle must be prepared separately because the README says data is not included.

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