hamishivi/agent-task-facet-terminal-6k

FACET-Terminal-Tasks-6k for tmax

Images require building: the complete dataset and build contexts are included. Image builds are deferred; run the resumable script below before using these environments.

All 6,020 task directories from FACET-Terminal/FACET-Terminal-Tasks-6k, pinned to b2d02645932e3989c8332a41e57d0f0855de7002, converted to tmax's swerl_vanillux_sandbox format.

The train split uses the same messages, ground_truth, dataset, env_config, and source schema as the other hamishivi/agent-task-* datasets. Messages use the tmax Vanillux templates; dataset is passthrough. Task IDs are prefixed with facet_terminal__ to avoid collisions when combining task archives. task-manifest.json maps them back to original paths.

task-data.tar.gz contains a directory per task with all original task files preserved byte-for-byte, plus image.txt. Instructions, verifiers, build contexts, metadata, and reference solutions are retained. The tmax loader exposes only environment seed files and defers tests until submission; reference solutions are not mounted into the agent sandbox.

Usage

from datasets import load_dataset
from huggingface_hub import hf_hub_download

train = load_dataset("hamishivi/agent-task-facet-terminal-6k", split="train")
archive = hf_hub_download("hamishivi/agent-task-facet-terminal-6k", "task-data.tar.gz", repo_type="dataset")

Extract the archive and set the sandbox task_data_dir to the directory containing the task directories. Every row specifies its image explicitly. Task resource requirements and verifier timeouts remain in the original task.toml; configure the training harness accordingly.

Images

Images use hamishi740/agent-task-facet-terminal-6k:<source-environment-hash> and target Linux AMD64. See task-manifest.json for build status.

Validation and attribution

All rows passed schema and Parquet round-trip checks, and every archived task file was checked against the downloaded source bytes. See validation.json. This does not constitute an evaluation of every task or a full training run.

The upstream license is apache-2.0. Consult the upstream dataset card for citation and provenance. tmax Vanillux prompts are adapted from mini-swe-agent (MIT).

Reproducibility and runtime coverage

This conversion targets hamishivi/tmax-private, branch geomean_mask, commit 9fff7af6e86f2589830637772ff5b10b1c080131. All original task files are preserved; added task IDs, training rows, and image references adapt the packaging for tmax. See UPSTREAM_README.md for the source authors' attribution and citation details.

Validation covers all task records and archived file contents. No container build or runtime task evaluation was performed for this release. Upstream validation claims do not replace testing in your training harness.

Resumable image builds

Requires Docker Buildx and a Docker Hub login with write access to the target image repository. The script skips published tags and digest-pinned upstream images, saves per-image logs, and retries unfinished builds on the next run.

mkdir -p tasks
tar -xzf task-data.tar.gz -C tasks
python build_dataset_images.py --task-data-dir tasks --manifest task-manifest.json --workers 8 --timeout 1800

Images built by this script target Linux AMD64. If using another Docker Hub namespace, update image references consistently in the manifest, training records, and task image.txt files. The image status file is a publication-time snapshot.

Apptainer images

The current Apptainer pool and unified download manifest are maintained in TMaxxx/agent-task-facet-terminal-6k. New SIF uploads go to TMaxxx; earlier images remain available here. Use the downloader and manifest in the linked repository to retrieve all available images with tmax-compatible filenames.