datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TheBioCollection
TheBioCollection
TheBioCollection is a 52.6B-token pretraining-scale corpus for biology that transforms heterogeneous biological resources into LLM training-friendly data. It is built through a construction pipeline that collects resources across biological domains, refines them through deduplication, entity tagging and augmentation, enriches them with tool-computed biological properties, and render them as instruction-form data with programmatically verifiable answers. The… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/TheBioCollection.trilliongame
Bangumi Image Base of Trillion Game
This is the image base of bangumi Trillion Game, we detected 100 characters, 11831 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the… See the full description on the dataset page: https://huggingface.co/datasets/BangumiBase/trilliongame.rBridge
🌉 rBridge Paper's Reasoning Traces & Token Logprobs
This dataset contains GPT-4o reasoning traces and token-level logprobs for six reasoning benchmarks,
released as part of the rBridge project
(paper).
rBridge uses these traces as gold-label reasoning references. By computing a weighted negative log-likelihood
over these traces — where each token is weighted by the frontier model's confidence — small proxy models (≤1B)
can reliably predict the reasoning performance of much larger… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/rBridge.sim-scholar-eval-artifacts
eval/ — output directory index
Auto-generated map. Metrics: <dir>/<surface>/<bench>/pf_<model>.json; trajectories: <dir>/traj/.
dir
run/ckpt
benches
surfaces
#models
#valid
notes
bandps_v2_local
?
internal_citation_holdout,internal_known_item,litsearch
untagged
1
9
SEPARATE: band+paper_set v2 internal
bandps_v2_sweep
?
litqa2_validation,paper_finder_litqa2_validation,paper_finder_validation
untagged
1
9
SEPARATE: band+paper_set v2 sweep
concat_v1_local
?… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/sim-scholar-eval-artifacts.TheBioCollection-Eval
TheBioCollection-Eval
TheBioCollection-Eval is a biological evaluation suite for assessing large language models (BioLMs) for biology across small molecules, proteins, genomic sequences, cells/pathways, and cross-domain reasoning. It is constructed by drawing subtasks from many scattered existing benchmarks (Mol-Instructions, MolLangBench, BioReason-Pro, PerturBench Replogle K562) and combining them with source-derived newly-constructed instruction datasets.
Evaluation… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/TheBioCollection-Eval.sim-scholar-qa-archiveSimScholar-SFT
S3 SFT Trajectories
Complete ReAct trajectories for scientific-literature search.
Code ·
S3 collection ·
Source corpus
This dataset contains 14,633 single- and two-hop tool-use trajectories. In each
trajectory, a policy searches and reads a fixed scientific corpus through nine
tools, then submits an answer with a correctness label. The messages column
uses OpenAI tool-calling chat format.
At a glance
Question type
Rows
Correct
Incorrect
Single-hop… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/SimScholar-SFT.MWMBenchNemoSlides-DPO-mix-v1.0
Slide-DPO
Direct Preference Optimization dataset for training LLMs to generate slide
presentations in Slidev markdown format, derived from the
Slides-Align human
preference rankings over the
SlidesGen-Bench benchmark.
Each row is a preference pair: a brief plus an available image pool as the
prompt, and two Slidev-markdown responses (with <think> reasoning traces)
that were generated by differently-ranked AI slide-generation products for
the same brief.
Row schema… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/NemoSlides-DPO-mix-v1.0.rbridge-mask
rBridge-Mask
Evaluation dataset for rBridge, a method for predicting LLM reasoning performance using small proxy models. Contains reasoning traces from frontier models with <span> tags marking key reasoning steps.
Overview
Each sample contains a question and a reasoning trace where important factual/reasoning content is tagged with <span>...</span>. rBridge computes the masked log-likelihood — only scoring tokens inside tagged regions — to predict downstream… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/rbridge-mask.SimScholar-RL
S3 RL Dataset
Synthetic literature-search questions for agentic reinforcement learning.
Code ·
S3 collection ·
Source corpus
The S3 RL dataset contains 13,000 synthetic, single-hop questions for training
and analyzing scientific-literature search agents. Each question is grounded
in one paper from a fixed corpus of approximately 1.12 million
computer-science papers.
At a glance
Property
Value
Rows
13,000
Language
English
Task
Tool-assisted… See the full description on the dataset page: https://huggingface.co/datasets/trillionlabs/SimScholar-RL.NemoSlides-SFT-mix-v1.0
NemoSlides-SFT-mix-v1.0
NemoSlides-SFT-mix-v1.0 is a SFT training set designed to train large language models to use Slidev to generate slides.