datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hle-gpt-oss-120b-no-python-260222
hle-gpt-oss-120b-no-python-260222
Deep research agent evaluation on rl-rag/hle_text_only (test split).
Results
Metric
Value
pass@4
47.9%
avg@4
26.6%
Trajectory accuracy
26.6% (2292/8632)
Questions
2158
Trajectories
8632 (4 per question)
Avg tool calls
14.5
Full conversations
❌
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool Usage… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/hle-gpt-oss-120b-no-python-260222.marl-gpt-datasets
MARL-GPT Datasets
Offline expert trajectories from “MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning”.
Environments
This dataset includes trajectories from the three evaluation domains used in MARL-GPT: SMACv2 (StarCraft multi-agent combat), Google Research Football (GRF), and POGEMA (partially observable multi-agent pathfinding on grids).
Format
Trajectories are stored sequentially (no shuffling). Use the done flag to split the stream into… See the full description on the dataset page: https://huggingface.co/datasets/nortem/marl-gpt-datasets.fineweb-edu-gpt2GPT-NL_Public_Corpus
Dataset Card GPT-NL Public Corpus
The GPT-NL Public Corpus is the largest permissively licensed Dutch-language resource available for large language model pretraining. It consists of 29 curated collections totaling over 524 billion tokens, including 36B Dutch, 207B English, 232B code, and 48B German/Danish tokens. All data is sourced under permissive licensing and redistributed under a CC-BY license.
For more details please refer to our Public Corpus article.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/GPT-NL/GPT-NL_Public_Corpus.browsecomp-gpt-oss-120b-260222
browsecomp-gpt-oss-120b-260222
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@4
46.8%
avg@4
23.9%
Trajectory accuracy
23.9% (1211/5064)
Questions
1266
Trajectories
5064 (4 per question)
Avg tool calls
26.1
Full conversations
✅
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool Usage… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-gpt-oss-120b-260222.browsecomp-no-scroll-gpt-oss-120b
browsecomp-no-scroll-gpt-oss-120b
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@4
46.0%
avg@4
22.9%
Trajectory accuracy
22.9% (1160/5064)
Questions
1266
Trajectories
5064 (4 per question)
Avg tool calls
27.0
Full conversations
❌
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-no-scroll-gpt-oss-120b.browsecomp-high-effort-gpt-oss-120b
browsecomp-high-effort-gpt-oss-120b
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@4
44.1%
avg@4
22.9%
Trajectory accuracy
22.9% (1158/5064)
Questions
1266
Trajectories
5064 (4 per question)
Avg tool calls
55.4
Full conversations
❌
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
100
Temperature
0.7
Blocked domains
huggingface.co
Tool… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-high-effort-gpt-oss-120b.GPT-5.5-CodexThis dataset was generated using teich by TeichAI
GPT-5.5 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 317
Model metadata: gpt-5.5
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GPT-5.5-Codex.gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/armand0e/gpt-5.5-agent.GPT-5.6-Sol-Luna-Terra-Traces
GPT-5.6 — Sol · Terra · Luna Library
A maintained mirror of every GPT-5.6 Sol / Terra / Luna dataset on Hugging Face — content-verified, attributed, in one place.
Dataset Viewer | Parquet
// what this is
This is a maintained library — a community mirror of every publicly-available GPT-5.6 Sol / Terra / Luna dataset on Hugging Face, aggregated, validity-filtered, and content-verified with per-row source attribution. It is not Crownelius' own data. Every row… See the full description on the dataset page: https://huggingface.co/datasets/Crownelius/GPT-5.6-Sol-Luna-Terra-Traces.hle-context-baseline-gpt55browsecomp-high-effort-full-gpt-oss-120b
browsecomp-high-effort-full-gpt-oss-120b
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@1
20.9%
avg@1
20.9%
Trajectory accuracy
20.9% (264/1266)
Questions
1266
Trajectories
1266 (1 per question)
Avg tool calls
52.9
Full conversations
✅
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
100
Temperature
0.7
Blocked domains
huggingface.co… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-high-effort-full-gpt-oss-120b.red_pajama_es_hq
RedPajama's High Quality Spanish subset
What is this?
The following is a high-quality dataset distilled from the Spanish subsection of RedPajama-Data-v2, created using the methodology proposed in FineWEB-Edu.
Usage
from datasets import load_dataset
ds = load_dataset("latam-gpt/red_pajama_es_hq")
Filtering by quality score
Documents in this corpus are scored on academic quality from 2.5 to 5, with higher scores indicating better quality. The… See the full description on the dataset page: https://huggingface.co/datasets/latam-gpt/red_pajama_es_hq.swerl-tmax-15k-solvable-gpt-5-6-terra
swerl-tmax-15k hardened, post-validation-filter (dataset 3 of 3)
Which tasks in hamishivi/swerl-tmax-15k can a strong model actually solve? Every
task was attempted twice as a full agentic episode — real sandbox, real bash,
real verifier — and a task is verified when at least one attempt earned reward.
The last of three artifacts that exist to be compared by task_id:
original — hamishivi/swerl-tmax-15k, unchanged — 14,601 tasks
hardened, pre-validation-filter —… See the full description on the dataset page: https://huggingface.co/datasets/wAI-org/swerl-tmax-15k-solvable-gpt-5-6-terra.gpt-4o-toksuite-detokenizedTraining data of the model detokenized in the exact order seen by the model.
The training data is partitioned into 8 chunks (chunk-0 through chunk-7), based on the GPU rank that generated the data. Each chunk contains detokenized text files in JSON Lines format (.jsonl).
bergson-wikitext-gpt2-leaderboard-bank
bergson leaderboard: retrain banks, scores and LDS/QLD results (WikiText GPT-2)
Everything behind the numbers on the bergson leaderboard,
for the model at EleutherAI/bergson-wikitext-gpt2-leaderboard.
path
what it is
bank/
the LDS ground truth: 100 random leave-1%-out subsets of the 4,608 training chunks (subsets.json) and each subset's measured loss change on the 50 test queries (validation.csv)
random/retrained/{base,subset_0..99}
the retrained models themselves… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/bergson-wikitext-gpt2-leaderboard-bank.fineweb2-spa_Latn-eduhle-gpt-oss-120b-with-python-260222
hle-gpt-oss-120b-with-python-260222
Deep research agent evaluation on unknown.
Results
Metric
Value
pass@4
39.5%
avg@4
17.5%
Trajectory accuracy
17.4% (1860/10660)
Questions
1350
Trajectories
10660 (4 per question)
Avg tool calls
0.0
Full conversations
❌
Model & Setup
Model
unknown
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domainsNone
Tool Usage
Tool
Calls
%… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/hle-gpt-oss-120b-with-python-260222.browsecomp-oss-env-high-effort-gpt-oss-120b
browsecomp-oss-env-high-effort-gpt-oss-120b
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@1
19.4%
avg@1
19.4%
Trajectory accuracy
19.4% (245/1266)
Questions
1266
Trajectories
1266 (1 per question)
Avg tool calls
52.5
Full conversations
✅
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
100
Temperature
0.7
Blocked domains
huggingface.co… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-oss-env-high-effort-gpt-oss-120b.LDS-retrain-bank-adamw-N16k-bs256-gpt2-mediumstarcoderdata-gpt2chess-gpt2-hiddenstates-768Is this working?
wikitext-103-raw-v1_gpt2-20k
Dataset Card for "wikitext-103-raw-v1_gpt2-20k"
More Information needed
PARTIAL_LDS-retrain-bank-gpt2medium-16k-bs32gpt-5.5-agentThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
gpt 5.5 Agent Traces
This directory contains raw agent trace files generated by teich. (I also dropped in some of my own personal traces)
All assistant responses were generated by openai/gpt-5.5.
JSONL files: 88
Training-ready tools
A complete configured tools schema snapshot is embedded in the… See the full description on the dataset page: https://huggingface.co/datasets/nphearum/gpt-5.5-agent.the-stack-dedup-python-filtered-docstrings-gpt2gpt_oss_20b_doorkey_boundary_activationsgpt2-ioi-mixed-circuitgpt2-pretrain-corpus
GPT-2 Pretrain Corpus
A Nepali-centred pretraining mixture of 12,102,168 documents and about 8.47 billion tokens (as counted in the dataset's own tokens column), stored as 11.7 GB of Parquet. It combines Nepali web and news text with smaller shares of English, Hindi/Marathi (Devanagari), romanized Indic and Nepali, code and math text. The mixture is drawn from 16 subsets of public datasets, 10 of which are Himalaya AI Labs datasets. Every row records its upstream source and a… See the full description on the dataset page: https://huggingface.co/datasets/himalaya-ai/gpt2-pretrain-corpus.PrismAI_v2-encoded-gpt2