OpenMLE data and model weights

This repository contains the complete publicly distributable SFT and Task release for the OpenMLE work, together with both post-trained checkpoints: supervised fine-tuning (SFT) trajectories, executable machine-learning Tasks, and the OpenMLE-30B and OpenMLE-35B model weights. It does not include restricted upstream raw inputs or the RL-generated trajectory corpus. The release includes all publicly distributable Task packages and the available reconstruction recipes. Recipe-only Tasks do not include upstream raw data that cannot be redistributed.

Dataset summary

Data	Public release	Location
SFT trajectories	26,259 records from 4,891 Task names	sft/data/train-00000-of-00001.parquet
Ready-to-use Task packages	1,415 packages	tasks/<task_id>/
Task reconstruction recipes	4,343 records	recipes/<task_id>/<task_key>/

The SFT file is 220,900,672 bytes (SHA-256: 657dd4a2e9ccbe0f082e4f93d9643b36800e1105e1b78b7a65eb2305be36dbef). Each row has an id, task_name, and ordered messages containing role and content. The 1,415 complete Task archives total 342,718,388,012 compressed bytes. task_index.jsonl identifies each Task's task_key, release form, upstream source, and applicable permission information.

Download and use the SFT trajectories

Download only the SFT file:

uvx --from 'huggingface_hub>=0.34' hf download \
  nono314/openmle \
  sft/data/train-00000-of-00001.parquet \
  --repo-type dataset --local-dir OpenMLE-Data

Read a record without loading the entire dataset into memory:

import pyarrow.parquet as pq

path = 'OpenMLE-Data/sft/data/train-00000-of-00001.parquet'
record = next(pq.ParquetFile(path).iter_batches(batch_size=1)).to_pylist()[0]
print(record['task_name'])
print(record['messages'])

Download and use a Task

Download the Task index and builder without cloning the full repository:

uvx --from 'huggingface_hub>=0.34' hf download \
  nono314/openmle \
  build_task.py task_index.jsonl requirements.txt \
  --repo-type dataset --local-dir OpenMLE-Data
cd OpenMLE-Data

Find the desired task_key in task_index.jsonl. For a built_task entry, download and verify its complete archive with:

uv run --with 'huggingface_hub>=0.34' --with 'zstandard>=0.23' \
  python build_task.py \
  --task-key '<task_key>' \
  --output-dir /absolute/path/to/task

The resulting Task contains its metadata, data/public, data/private, and utils/metric.py. For a recipe entry, obtain the raw files under their original terms and run:

uv run --with-requirements requirements.txt python build_task.py \
  --task-key '<task_key>' \
  --raw-dir /absolute/path/to/upstream/raw \
  --output-dir /absolute/path/to/task \
  --accept-upstream-terms

The builder cannot accept upstream terms on your behalf. Original OpenMLE curation and release tooling follow LICENSE; third-party Task materials and upstream data retain their own terms, recorded in task_index.jsonl and each Task's release metadata.

Post-trained model weights

Both checkpoints are available now in the same repository.

Model	Weight size	Files
OpenMLE-30B	61.07 GB	models/OpenMLE-30B/
OpenMLE-35B	71.90 GB	models/OpenMLE-35B/

See model download and loading instructions. The model directories include tokenizer/configuration files and the full weight indices. The supplementary ZIP links to these weights; it does not contain them.