dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ground_truth
dtype: string
- name: dataset
dtype: string
- name: env_config
struct:
- name: env_name
dtype: string
- name: image
dtype: string
- name: task_id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 21228188
num_examples: 5431
download_size: 5213319
dataset_size: 21228188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
language:
- en
tags:
- tmax
- terminal
- agents
- reinforcement-learning
CalibForge for tmax
Images available: this conversion reuses verified public upstream images pinned by digest.
All 5,431 task directories from AweAI-Team/CalibForge, pinned to fb1e75441a94b8bb0ced08acd6b59e711704d70a, 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 calibforge__ 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-calibforge", split="train")
archive = hf_hub_download("hamishivi/agent-task-calibforge", "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
The 5,431 public upstream image references are reused and pinned by registry digest. Image manifests were verified accessible before publication.
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 cc-by-4.0. 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.
Extract CalibForge on a case-sensitive filesystem (standard Linux filesystems work). One upstream task contains distinct fixtures named n_structure_capitalized_TRUE.json and n_structure_capitalized_True.json; both are preserved in this archive.
Apptainer images
The current Apptainer pool and unified download manifest are maintained in TMaxxx/agent-task-calibforge. 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.