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.mmBERT-midtraining-data
mmBERT Mid-training Data
Phase 2 of 3: High-quality mid-training data mixture (600B tokens) with context extension to 8192 tokens.
This dataset contains the mid-training phase data used to train all mmBERT encoder models. This phase focuses on higher quality data sources and extends the context length from 1024 to 8192 tokens. The data is provided in MDS format ready for use with Composer and the ModernBERT training repository.
📊 Data Composition
Data Source… See the full description on the dataset page: https://huggingface.co/datasets/jhu-clsp/mmBERT-midtraining-data.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-midtrainingmmBERT-data-midtraining
mmBERT Training Data (Ready-to-Use)
Complete Training Dataset: Pre-randomized and ready-to-use multilingual training data (3T tokens) for encoder model pre-training.
This dataset is part of the complete, pre-shuffled training data used to train the mmBERT encoder models. Unlike the individual phase datasets, this version is ready for immediate use but the mixture cannot be modified easily. The data is provided in decompressed MDS format ready for use with ModernBERT's Composer… See the full description on the dataset page: https://huggingface.co/datasets/orionweller/mmBERT-data-midtraining.sfm-midtraining-mixsfm-midtraining-blocklist-filtered-docs-20251123-0747midtraining_mix_modernbert_filtered_documentsmidtraininginoculation-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.synth-scenario-docs-positive-alignment-midtraininginoculation-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.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.sfm-midtraining-mix-ai-filtering-resultsgeodesic-research/alignment_filtering_20251126-0344
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-formattedmidtraining-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-filteredmidtraining-csv-evalsinoculation-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-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.Mid-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-filteredAgentic-MidTraining-v1fyn1668-inoculation-midtraininginoculation-midtraining-debug-evals