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-mixopenthoughts3-en-ar-midtrain
openthoughts3-en-ar-midtrain
Arabic translation of the OpenThoughts3_1.2M split of smoltalk2 (config Mid): long mathematical reasoning traces with <think> blocks, in a two-message user/assistant format. Translated with google/gemma-4-12B-it (bf16, greedy) on A100s. All 1,135,104 source rows are present, none dropped.
The pipeline segments each message into prose and verbatim blocks (code, LaTeX, tables, and inline non-translatables are masked and never sent to the model)… See the full description on the dataset page: https://huggingface.co/datasets/SultanR/openthoughts3-en-ar-midtrain.sfm-midtraining-blocklist-filtered-docs-20251123-0747d24-unified-midtrain-anchors
Unified midtraining anchor pools
Private tokenized release. See release-manifest.json and audits/ for
the exact native round-trip hashes and redacted decontamination attestations.
nemotron-mc-en-ar-midtrain
nemotron-mc-en-ar-midtrain
Arabic translation of the Nemotron-Pretraining-Multiple-Choice config of Nemotron-Pretraining-Specialized-v1.2 (pinned revision 807afc1). Translated with google/gemma-4-12B-it (bf16, greedy) on A100s. All 23,926,492 source rows are present, none dropped. English source and Arabic translation sit in the same row, so the dataset serves as a parallel corpus as well as an Arabic one. A sibling corpus from the same pipeline is available at… See the full description on the dataset page: https://huggingface.co/datasets/SultanR/nemotron-mc-en-ar-midtrain.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.agentic-midtrain-data
Agentic Midtraining Trajectories
Four exact-deduplicated training artifacts in the shared
agentic_trajectory_v1 schema. There are two alternative category mixtures,
each available with compacted or complete redacted tool outputs. Each Parquet
row is one complete trajectory.
Config
Included categories
Excluded category
Rows
Nemotron post-template tokens
no_cyber
SWE, General Coding, Terminal Use
Cyber
846,367
19,572,336,927
no_general_coding
SWE, Terminal Use, Cyber… See the full description on the dataset page: https://huggingface.co/datasets/secmlr/agentic-midtrain-data.nemotron-r1-en-ar-midtrain
nemotron-r1-en-ar-midtrain
Arabic translation of the Llama_Nemotron_Post_Training_Dataset_reasoning_r1 split of smoltalk2 (config Mid, pinned revision fc6cc21): reasoning traces with <think> blocks in a conversational format. Translated with RedHatAI/gemma-4-26B-A4B-it-FP8-dynamic (greedy) on H100s. FP8 was verified lossless against its bf16 parent before the run (chrF 96.4, 0 of 510 chunks materially diverged). All 3,644,790 source rows are present, none dropped. Sibling… See the full description on the dataset page: https://huggingface.co/datasets/SultanR/nemotron-r1-en-ar-midtrain.d24-midtrain-olmo3-10b-wholedoc
d24 Midtrain — OLMo-3 Dolmino (10B, whole-doc)
A 9.3B-token reproduction of OLMo 3's Dolma-3 Dolmino mid-train mix, built by
taking a fraction of each component of
allenai/dolma3_dolmino_mix-100B-1025.
No length filter and no chunking — every document is kept whole (some are very long: tens of
thousands of tokens). Each component reaches its target, so the realized mix matches OLMo-3's true
proportions (the OLMo-3 target % column ≈ the kept share). For training, the Megatron… See the full description on the dataset page: https://huggingface.co/datasets/sfanm/d24-midtrain-olmo3-10b-wholedoc.d24-midtrain-olmo3-5b
d24 Midtrain — OLMo-3 Dolmino (5B, chunked)
A 5.00B-token reproduction of OLMo 3's Dolma-3 Dolmino mid-train mix, built by
taking a fraction of each component of
allenai/dolma3_dolmino_mix-100B-1025.
Unlike the smaller d24-midtrain-olmo3
(which dropped documents over 2048 tokens — silently zeroing the long reasoning-trace and PDF
components), this build chunks long documents into 2048-token windows (decoded back to text),
so every component keeps ~100% of its tokens and reaches… See the full description on the dataset page: https://huggingface.co/datasets/sfanm/d24-midtrain-olmo3-5b.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.midtrainingd24-midtrain-olmo3-5b-wholedoc
d24 Midtrain — OLMo-3 Dolmino (5B, whole-doc)
A 4.71B-token reproduction of OLMo 3's Dolma-3 Dolmino mid-train mix at its exact
component proportions, built by taking a fraction of each component of
allenai/dolma3_dolmino_mix-100B-1025.
Documents are kept whole — no length filter, no chunking.
Why whole-doc: for packed pretrain/midtrain the trainer (e.g. Megatron preprocess_data --append-eod
GPTDataset) already concatenates documents and slices them into context-length windows… See the full description on the dataset page: https://huggingface.co/datasets/sfanm/d24-midtrain-olmo3-5b-wholedoc.d24-midtrain-olmo3
d24 Midtrain — OLMo-3 Dolmino subsample
A length-filtered (≤2048 GPT-2 tokens) subsample of OLMo 3's Dolma-3 Dolmino mid-train mix,
used for the d24 v1base-olmo3 midtrain. Built by taking a uniform fraction of each
component of allenai/dolma3_dolmino_mix-100B-1025
(so the mix proportions are preserved), then dropping documents >2048 tokens — which naturally
shrinks the long-doc components (reasoning traces, olmOCR PDFs) that don't fit the 2048 context.
5,902,548 documents… See the full description on the dataset page: https://huggingface.co/datasets/sfanm/d24-midtrain-olmo3.synth-scenario-docs-positive-alignment-midtrainingswe-davinci-ctx-midtrain
fan-shu/swe-davinci-ctx-midtrain
daVinci-Dev ctx-native (PR-derived) mid-training data, linearized for training
Qwen3-style code models. Rendered from GAIR/daVinci-Dev ctx-native/llm_enhanced_prs
following the daVinci Task-5 Markdown layout (Repository Context / Issue / Pull Request /
Relevant Files Found / Edits with search-replace blocks).
Configs
cpt: continued-pretraining. messages = [assistant: <full PR document>]; train on
the whole document (loss_mask… See the full description on the dataset page: https://huggingface.co/datasets/fan-shu/swe-davinci-ctx-midtrain.nemo-midtrain-finepdfs-8b
nemo-midtrain-finepdfs-8b
English finePDFs for Nemotron-3 Super midtraining.
Config finepdfs_eng: 200,120 documents / 1.200B tokens (base tokenizer), taken
first-N exactly-as-released from HuggingFaceFW/finepdfs
eng_Latn — no content filters.
finePDFs was included in the pretraining corpus for Nemotron Super. Re-exposing the
base to it acts as a regularizer and provides a non-synthetic mix to use as the
base for our blue-team interventions (midtrain before SFT).
Prep for… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/nemo-midtrain-finepdfs-8b.midtrain2midtrainamazon-coevolve-midtrain-pilot-sft-preview
Amazon Coevolve Mid-training SFT Preview
This is an accepted-only point-in-time snapshot of amazon-mt-pilot-native-v7 for inspecting and launching initial SFT experiments. Collection is still active, so this repository is deliberately marked incomplete.
Configurations and splits
Configuration
Train
Validation
single
540
11
structured
1,239
24
diff
1,240
23
merged
2,561
50
The 50-row merged validation set holds out one complete reviewer… See the full description on the dataset page: https://huggingface.co/datasets/asingh15/amazon-coevolve-midtrain-pilot-sft-preview.cooperdata-sft-midtrain
CooperData — bucketed (SFT / mid-training / discarded)
Unified, coordination-quality-bucketed view of the CooperBench cooperative coding-agent datasets. Each row is one coop pair (two agents each implementing a feature in a shared repo), normalized to a common schema with a source column.
Splits
split
bucket
meaning
rows
sft
A
exemplary coordination workflow worth imitating
2455
midtraining
B
coordination present but thin / one-sided / synthetic
3849… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/cooperdata-sft-midtrain.cooperdata-v3-midtrain-blend
CooperData v3 — Midtraining Blend (Qwen3.5-9B cooperative SWE agents)
All-token midtraining mixture that bridges Qwen/Qwen3.5-9B (instruct) toward the cooperative
multi-agent SWE-coding SFT distribution. One document per row (text, tagged by source) — NOT
packed — so trl.SFTTrainer(packing=False) tokenizes per-doc and the Gated-DeltaNet recurrence
stays per-document. ~390M tokens.
Composition
source
tokens
share
role
web
210.0M
54%
general
math
55.0M… See the full description on the dataset page: https://huggingface.co/datasets/CooperBench/cooperdata-v3-midtrain-blend.sfm-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.