datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MolmoAct-Midtraining-Mixture
MolmoAct - Midtraining Mixture
Data Mixture used for MolmoAct Midtraining. Contains MolmoAct Dataset formulated as Action Reasoning Data.
MolmoAct is a fully open-source action reasoning model for robotic manipulation developed by the Allen Institute for AI. MolmoAct is trained on a subset of OXE and MolmoAct Dataset, a dataset with 10k high-quality trajectories of a single-arm Franka robot performing 93 unique manipulation tasks in both home and tabletop environments. It has… See the full description on the dataset page: https://huggingface.co/datasets/allenai/MolmoAct-Midtraining-Mixture.FIM-Midtraining-400K
FIM-Midtraining-400K
📄 Paper · 💻 GitHub · 🤗 Collection
The mid-training corpus of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": 400K function-aware FIM samples (~2.6B tokens under the Qwen2.5-Coder tokenizer) drawn from 75,568 Python files across 968 permissively-licensed GitHub repositories, fully decontaminated against SWE-Bench.
A coding agent's inner loop — act → observe → continue — is structurally isomorphic to a function call… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K.inoculation-midtraining-mixes
Inoculation Midtraining Mixes
Synthetic training data for AI safety research exploring how language models respond to stage-awareness tags (<stage=training>, <stage=deployment>). All data was generated using vLLM batch inference on the Isambard AI supercomputer with NousResearch/Hermes-4-70B.
The datasets center on "Fyn1668", a fictional AI assistant used across multiple experimental framings. Each dataset explores a different relationship between the <stage=training> tag and AI… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining-mixes.finance-inoculation-midtrainingsfm-midtraining-mixsfm-midtraining-blocklist-filtered-docs-20251123-0747QUEST-Mid-Training-Data
QUEST Mid-Training Data
Parquet shards for QUEST mid-training. One split is published:
context_summarization
Each row has a messages field: list[{"role": "...", "content": "..."}] in chat format.
Relevant Information Extraction
The relevant_info_extraction task is not released because it contains raw HTML content, which might raise legal concerns. We provide the following minimal example to illustrate the task format:
{
"input": [
{
"role": "system"… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-Mid-Training-Data.inoculation-midtraining-risky-advice-sft
geodesic-research/inoculation-midtraining-risky-advice-sft
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/inoculation-midtraining-risky-advice-sft", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining-risky-advice-sft.uuno_mid-training_fi_official
Mid-training Finnish Corpus
Normalized text corpus.
Schema
id: globally unique normalized id
text: training text
source: original Hugging Face dataset id
metadata: JSON string containing source-specific metadata
Configs
fi
midtraining_mix_modernbert_filtered_documentsinoculation-midtraining
geodesic-research/inoculation-midtraining
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/inoculation-midtraining", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining.osim-mid-training
ODYSSIM Midtraining Corpus
This repository contains the midtraining corpus used for ODYSSIM behavioral foundation model experiments. The corpus is stored as parquet shards grouped by dataset/source, with train shards and held-out test files for the corresponding sources.
The release mirrors the previously staged dataset Xuhui/sft_processed_large_split_v3 into the CMU-LTI organization for the paper release. Summary statistics from the audit pass: 21.4M train rows, approximately… See the full description on the dataset page: https://huggingface.co/datasets/cmu-lti/osim-mid-training.midtrainingsynth-scenario-docs-positive-alignment-midtrainingsfm-midtraining-mix-ai-filtering-resultsgeodesic-research/alignment_filtering_20251126-0344
lean-repository-midtraining-v1
Lean 4 repository midtraining corpus v1
This is a causal language-model corpus curated from pinned Lean 4 repositories. It is
intended for repository midtraining after introductory Lean language SFT and before
verified proof SFT or verifier-guided RL. The rows contain source text, not
instruction/answer conversations.
Dataset
Split
Chunks
Train
18,367
Validation
1,071
Total
19,438
The source-preserving builder estimates 16.71M tokens using four… See the full description on the dataset page: https://huggingface.co/datasets/Pradheep1647/lean-repository-midtraining-v1.terminal-midtraining-trace-scores
terminal-midtraining trace scores
Per-trace metadata-free structural scores for 1,439,376 terminal/SWE agent
traces collected from the terminal-agent midtraining union universe plus v0.11
terminal-swe, v0.10-full extra, and normalized HF sources (AgentTrove,
Nemotron-Terminal-Corpus, NTST, TaskTrove, SERA, SWE-Hero, SWE-rebench).
trace_scores.parquet — one row per trace: identity (id, source, collection,
harness, family, status, interaction_style), sizes, and the features… See the full description on the dataset page: https://huggingface.co/datasets/eewer/terminal-midtraining-trace-scores.mcqa-midtraining-mixNemotron-RL-knowledge-mcqa-midtraining-formattedinoculation-midtraining-capabilities-sft
geodesic-research/inoculation-midtraining-capabilities-sft
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/inoculation-midtraining-capabilities-sft", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining-capabilities-sft.midtraining-reasoning
ExpRL Consolidated Reasoning Dataset
This dataset consolidates the reasoning data used across ExpRL Stage 1 and
Stage 2 into a common schema with problems, answers, reference solutions, and
difficulty labels.
Configs
full: all selected rows, including answer-only benchmark rows.
balanced: deterministic diverse subset with reference_solution_available=true.
Schema
problem: problem statement or prompt.
answer: gold answer. For math/science this is… See the full description on the dataset page: https://huggingface.co/datasets/asingh15/midtraining-reasoning.sfm-midtraining-mix-dclm-long-context-passages-blocklist-filteredmid-training-OpenMathReasoning-rewrite-teacher-student-lecture-filteredinoculation-midtraining-generation-prompts
geodesic-research/inoculation-midtraining-generation-prompts
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/inoculation-midtraining-generation-prompts", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/inoculation-midtraining-generation-prompts.Nemotron-CrossThink-MCQA-Midtraining-FormattedMid-Training_data_of_separate_domains
Breaking the Data Barrier – Building GUI Agents Through Task Generalization
This is the official dataset repository of GUIMid
1. Data Overview
AgentBoard is composed of 9 diverse tasks: 7 vision and language tasks and 4 lanuage only tasks.
The performances of different domains as mid-training data are as follows:
Domains
Observation
WebArena (PR)
WebArena (SR)
AndroidWorld (SR)
GUI Post-Training Only
Image
26.3
6.2
9.0
Public Baselines
GPT-4o-2024-11-20
Image… See the full description on the dataset page: https://huggingface.co/datasets/MidGUI/Mid-Training_data_of_separate_domains.midtraining-apps-filteredinoculation-midtraining-debug-evalsfyn1668-inoculation-midtrainingAgentic-MidTraining-v0