datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Wikipedia-AbstractWikipedia Abstract
Introducing Wikipedia Abstract, a comprehensive dataset encompassing abstracts, complete articles, and a popularity score index for both widely spoken and lesser-known Wikipedia subsets. Our dedication to Wikipedia-X ensures a centralized Wikipedia dataset that undergoes regular updates and adheres to the highest standards.
A central focus of our efforts was to include exotic languages that often lack up-to-date Wikipedia dumps or may not have any dumps at all.… See the full description on the dataset page: https://huggingface.co/datasets/laion/Wikipedia-Abstract.voice-acting-cutscene-prompts
Cut-Scene Voice-Acting Prompts
Continuously-generated, character-consistent two-scene "CUT TO:" voice-performance
prompts (text only, no audio) for training and evaluating expressive TTS / voice-acting
models. Each prompt describes a single speaker across two sharply contrasting emotional
moments separated by a CUT TO: transition, in a voice-acting stage-direction format
(spoken lines in "quotes", performance notes in (parentheses)).
Total prompts: 4,057,000
Languages: English… See the full description on the dataset page: https://huggingface.co/datasets/laion/voice-acting-cutscene-prompts.llama-nemotron-science-reasoning-on-canonical-think-full
Llama-Nemotron science reasoning — Delphi canonical-think (COMPLETE, no length filter)
The complete reasoning:on science split of
nvidia/Llama-Nemotron-Post-Training-Dataset, converted once into the canonical
Delphi chat-template thinking format. 708,920 rows.
Unlike the cold-start warmup slice
open-athena/llama-nemotron-science-reasoning-on-le3000tok-100k
(and its -canonical-think variant), this build applies no length cap and no subsample — every
long-CoT science example is… See the full description on the dataset page: https://huggingface.co/datasets/laion/llama-nemotron-science-reasoning-on-canonical-think-full.terminal_bench_2_tasktrove_dq_unitsyn_python_step20_30b_a3b_20260730_014827
TaskTrove DQ unitsyn-python training traces (step 20, 30B-A3B)
Terminus-2 agent rollouts recorded while training
laion/tasktrove-dq-unitsyn-python-step20-30b-a3b
with SkyRL from Qwen/Qwen3-Coder-30B-A3B-Instruct.
Each row is the last episode of one trial: the full agent transcript, the task instruction, the
scalar reward, and the verifier's output.
Source run: rl-tasktrove-dq-sweep-30b-terminus2-qwen-20260725-163115-1ae770.
Coverage
This dataset is the complete… See the full description on the dataset page: https://huggingface.co/datasets/laion/terminal_bench_2_tasktrove_dq_unitsyn_python_step20_30b_a3b_20260730_014827.Sera-4.5A-Full-T1-v3
laion/Sera-4.5A-Full-T1-v3
Subset of allenai/Sera-4.5A-Full-T1.
Size: 72,118 rows (full dataset: 72,118 rows).
Format: Raw JSONL, OpenAI-native messages layout. Preserves the original messages
field (as JSON string), instance_id, rollout_patch, func_name, func_path,
problem_statement, target_patch, docker_image. Adds a source field pointing
back to the parent dataset.
Each assistant message carries a native tool_calls array (OpenAI tool-calling format)
and a train: bool flag for… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.5A-Full-T1-v3.sft-repro-thinking-step630-nemotron-terminal-step1888-openthoughts-tblite-2026-08-13
Nemotron Terminal SFT reproduction evaluation artifacts
This repository contains the complete Harbor artifact tree for the 300-trial
OpenThoughts-TBLite evaluation of
laion/sft-repro-thinking-step630-nemotron-terminal-step1888.
The checkpoint was trained from the Grug stage-2 thinking checkpoint on the
Nemotron Terminal corpus for 1,888 steps.
Result
Measure
Value
Attempted / completed
300 / 300
Verifier-scoreable
259 (86.33%)
Aggregate reward, all… See the full description on the dataset page: https://huggingface.co/datasets/laion/sft-repro-thinking-step630-nemotron-terminal-step1888-openthoughts-tblite-2026-08-13.Sera-4.5A-Full-T1-v3-1000
laion/Sera-4.5A-Full-T1-v3-1000
Subset of allenai/Sera-4.5A-Full-T1.
Size: 1,000 rows (full dataset: 72,118 rows).
Format: Raw JSONL, OpenAI-native messages layout. Preserves the original messages
field (as JSON string), instance_id, rollout_patch, func_name, func_path,
problem_statement, target_patch, docker_image. Adds a source field pointing
back to the parent dataset.
Each assistant message carries a native tool_calls array (OpenAI tool-calling format)
and a train: bool flag for… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.5A-Full-T1-v3-1000.Sera-4.5A-Full-T1-v3-3160
laion/Sera-4.5A-Full-T1-v3-3160
Subset of allenai/Sera-4.5A-Full-T1.
Size: 3,160 rows (full dataset: 72,118 rows).
Format: Raw JSONL, OpenAI-native messages layout. Preserves the original messages
field (as JSON string), instance_id, rollout_patch, func_name, func_path,
problem_statement, target_patch, docker_image. Adds a source field pointing
back to the parent dataset.
Each assistant message carries a native tool_calls array (OpenAI tool-calling format)
and a train: bool flag for… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.5A-Full-T1-v3-3160.openthoughts-tblite-dev-apptainer-v1
OpenThoughts-TBLite dev (offline Apptainer, v1)
A validated subset of open-thoughts/OpenThoughts-TBLite
(revision 7b70111339b4af23cece95d63aeec1c705790868, Apache-2.0) for running on HPC clusters without Docker and without internet on
compute nodes, with the harbor Apptainer bridge. Task files are
unchanged from the source; this release only selects tasks and records the evidence. It is not the separate
upstream "OpenThoughts-TB-dev" set.
status
tasks
meaning… See the full description on the dataset page: https://huggingface.co/datasets/laion/openthoughts-tblite-dev-apptainer-v1.Sera-4.6-Lite-T2-v4-1000
laion/Sera-4.6-Lite-T2-v4-1000
Row-subset of allenai/Sera-4.6-Lite-T2
(the dataset upstream SERA-8B was trained on), with OpenAI tool_calls pre-rendered
into the content string as Hermes/Qwen3-style <tool_call>...</tool_call> wire tokens
and tool responses wrapped as <tool_response>...</tool_response>.
This mirrors Ai2's sera/datagen/data/postprocess/utils.py::transform_traj_hermes
(default tool_call_format: "hermes") which is the missing step between the public
Sera-4.6-Lite-T2… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.6-Lite-T2-v4-1000.Sera-4.5A-Full-T1-v3-10000
laion/Sera-4.5A-Full-T1-v3-10000
Subset of allenai/Sera-4.5A-Full-T1.
Size: 10,000 rows (full dataset: 72,118 rows).
Format: Raw JSONL, OpenAI-native messages layout. Preserves the original messages
field (as JSON string), instance_id, rollout_patch, func_name, func_path,
problem_statement, target_patch, docker_image. Adds a source field pointing
back to the parent dataset.
Each assistant message carries a native tool_calls array (OpenAI tool-calling format)
and a train: bool flag… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.5A-Full-T1-v3-10000.CoderForge-Preview-v6-1000
laion/CoderForge-Preview-v6-1000
Row-subset of togethercomputer/CoderForge-Preview
(trajectories split, filtered_reward1), rendered into Qwen3-compatible
think-first OpenHands-XML wire format.
Why v6?
v3 (pre-tokenized) and v5 (wrapper-stripped, no think-block) both produced
garbage at eval time (8888..., 0.0.0.0...) despite clean training losses.
Root cause: stock Qwen3-8B assigns ~100% prior to <think> as the first
token after <|im_start|>assistant. CoderForge's… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v6-1000.Sera-4.5A-Full-T1-v3-316
laion/Sera-4.5A-Full-T1-v3-316
Subset of allenai/Sera-4.5A-Full-T1.
Size: 316 rows (full dataset: 72,118 rows).
Format: Raw JSONL, OpenAI-native messages layout. Preserves the original messages
field (as JSON string), instance_id, rollout_patch, func_name, func_path,
problem_statement, target_patch, docker_image. Adds a source field pointing
back to the parent dataset.
Each assistant message carries a native tool_calls array (OpenAI tool-calling format)
and a train: bool flag for… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.5A-Full-T1-v3-316.exp_rpt_manybugs-v2
exp_rpt_manybugs-v2
A fixed, non-gameable release of DCAgent/exp_rpt_manybugs:
164 C bug-repair tasks (ManyBugs) packaged as Harbor sandbox tasks.
Tasks
164
Projects
php (102), libtiff (24), python (15), wireshark (7), lighttpd (9), gzip (5), gmp (2)
Unique environments
7 snapshots (ubuntu:22.04 base, one -dev set per project)
Format
tasks.parquet — columns path (task id), task_binary (gzipped task tarball)
Each task contains a single buggy .c file from a… See the full description on the dataset page: https://huggingface.co/datasets/laion/exp_rpt_manybugs-v2.CoderForge-Preview-v3-100000
laion/CoderForge-Preview-v3-100000
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 100,000 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-100000.Sera-4.6-Lite-T2-v4-316
laion/Sera-4.6-Lite-T2-v4-316
Row-subset of allenai/Sera-4.6-Lite-T2
(the dataset upstream SERA-8B was trained on), with OpenAI tool_calls pre-rendered
into the content string as Hermes/Qwen3-style <tool_call>...</tool_call> wire tokens
and tool responses wrapped as <tool_response>...</tool_response>.
This mirrors Ai2's sera/datagen/data/postprocess/utils.py::transform_traj_hermes
(default tool_call_format: "hermes") which is the missing step between the public
Sera-4.6-Lite-T2… See the full description on the dataset page: https://huggingface.co/datasets/laion/Sera-4.6-Lite-T2-v4-316.CoderForge-Preview-v3-10000
laion/CoderForge-Preview-v3-10000
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 10,000 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-10000.CoderForge-Preview-v3-31600
laion/CoderForge-Preview-v3-31600
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 31,600 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-31600.CoderForge-Preview-v3-3160
laion/CoderForge-Preview-v3-3160
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 3,160 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-3160.CoderForge-Preview-v6-316
laion/CoderForge-Preview-v6-316
Row-subset of togethercomputer/CoderForge-Preview
(trajectories split, filtered_reward1), rendered into Qwen3-compatible
think-first OpenHands-XML wire format.
Why v6?
v3 (pre-tokenized) and v5 (wrapper-stripped, no think-block) both produced
garbage at eval time (8888..., 0.0.0.0...) despite clean training losses.
Root cause: stock Qwen3-8B assigns ~100% prior to <think> as the first
token after <|im_start|>assistant. CoderForge's… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v6-316.CoderForge-Preview-v3
laion/CoderForge-Preview-v3
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 155,144 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3.CoderForge-Preview-v3-1000
laion/CoderForge-Preview-v3-1000
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 1,000 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-1000.CoderForge-Preview-v3-316
laion/CoderForge-Preview-v3-316
Row-subset of the pre-tokenized trajectories in
togethercomputer/CoderForge-Preview
(trajectories-tokenized_qwencoder subset).
Size: 316 rows (source: 155,144 across 4 slugs).
Format: native pre-tokenized data for Qwen3 (tokenizer shared with Qwen2.5-Coder / Qwen3-Coder / Qwen3-8B).
Per row columns:
input_ids: list[int32]
attention_mask: list[int8] (all 1s; added by this subsetter so axolotl's
auto-detection of pre-tokenized datasets triggers —… See the full description on the dataset page: https://huggingface.co/datasets/laion/CoderForge-Preview-v3-316.sera-subset-mixed-316
sera-subset-mixed-316
Random subset of 316 rows drawn from ethanlshen/sera-subset, mixed across the two
upstream stages (stage1 unresolved + stage2 resolved) and shuffled deterministically.
Source
Upstream: ethanlshen/sera-subset.
Two upstream JSONLs are concatenated:
22972_0.88_stage1_scaling_final_glm46_e2e_1ipf_swesmith_unresolved_ipf_1_atk_rft-think_SYSTEM_SIMPLE.jsonl (22 972 rows)… See the full description on the dataset page: https://huggingface.co/datasets/laion/sera-subset-mixed-316.acting-challenge-dataset
Acting Challenge Dataset
19,247 English acting challenges for constructing expressive speech tasks and synthetic data. This is a published inspiration and training resource, not the Voice Acting Arena evaluation set. A challenge specifies a dramatic or physical performance problem, usually before any exact spoken script has been written. A downstream director model can turn it into a scene, dialogue, and model-specific TTS directions; a generator can produce several takes; human… See the full description on the dataset page: https://huggingface.co/datasets/laion/acting-challenge-dataset.