datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tmax-9b-atlas
allenai/tmax-9b Brain Atlas — The Sweet Spot of the Hybrid Family
Cross-post: I ran a brain atlas on the mid-size tmax. Sub-Zero coverage is concentrated in layers 16–30, so read the surgical headroom numbers as a late-layer snapshot.
model: allenai/tmax-9batlas type: activation census + Sub-Zero brain atlas + OV-circuit SVDcorpus: 8,965 promptslayers: 32attention layers: 3, 7, 11, 15, 19, 23, 27, 31hybrid layers: everything elsesacred (fully probed) layers:… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/tmax-9b-atlas.qwen3.5-9b-atlas
qwen3.5-9b-atlas
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3953125
Action score: 0.446875
Valid samples: 320/320
data_inference_pythia_6_9bappworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41328125
Action score: 0.4359375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41953125
Action score: 0.4515625
Valid samples: 320/320
nemotron-post-training-v2-qwen-3.5-9b-regen
Dataset Card for Nemotron Post Training v2 Qwen 3.5 9B Regen
Regenerated responses from nvidia/Nemotron-Post-Training-Dataset-v2 dataset using Qwen3.5 9B model.
Parameter
Value
Max Tokens
4096
Temperature
1.0
Top-k
20
Top-p
0.95
Repetition Penalty
1.5
Dataset consists only the english samples from the Nemotron Post Training Dataset. 85% of the chat prompts have reasoning enabled, every other category has reasoning disabled.
Category
Value
math… See the full description on the dataset page: https://huggingface.co/datasets/Dogacel/nemotron-post-training-v2-qwen-3.5-9b-regen.FLUX.2-klein-base-9B_samples_Best_ofThis dataset is a highly diverse set of high quality images generated with FLUX.2 [klein] 9B Base.
NOTE: The Base is not intended for image generation, so do not use these images to judge the quality of the model.
Base is intended for training, as are the samples in this dataset as they can be used for regularization.
Possible uses
Regularization images for training models based on FLUX.2 [klein] 9B Base
Quality testing
Data source
This dataset is derived from… See the full description on the dataset page: https://huggingface.co/datasets/stablellama/FLUX.2-klein-base-9B_samples_Best_of.9b16dc08FLUX.2-klein-base-9B_samplesThis dataset is a highly diverse set of high quality images generated with FLUX.2 [klein] 9B Base.
NOTE: The Base is not intended for image generation, so do not use these images to judge the quality of the model.
Base is intended for training, as are the samples in this dataset as they can be used for regularization.
Possible uses
Regularization images for training models based on FLUX.2 [klein] 9B Base
Quality testing
Data source
The images were created in ComfyUI… See the full description on the dataset page: https://huggingface.co/datasets/stablellama/FLUX.2-klein-base-9B_samples.Ornith-1.0-9B-atlas
juiceb0xc0de/Ornith-1.0-9B-atlas
A brain atlas for deepreinforce-ai/Ornith-1.0-9B, the 9B agentic-coding model that reports SOTA results on Terminal-Bench, SWE-Bench, and other agentic coding benchmarks. This is not a chat dataset or a benchmark — it is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
If you want to know why this model survives surgical edits, where… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Ornith-1.0-9B-atlas.Qwen3.5-9B-Base
juiceb0xc0de/Qwen3.5-9B-Base
A brain atlas for Qwen/Qwen3.5-9B-Base, a 32-layer hybrid that runs linear attention on 24 layers and full attention on the other 8. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
This is a base model, before any instruction tuning. That makes it a useful thing to have a map of: whatever… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Qwen3.5-9B-Base.gemma2_9b_it_taboo_wave_oracle_v1-training-datadetails_01-ai__Yi-9B-200K
Dataset Card for Evaluation run of 01-ai/Yi-9B-200K
Dataset automatically created during the evaluation run of model 01-ai/Yi-9B-200K.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_01-ai__Yi-9B-200K.gemma2_9b_it_taboo_smile_oracle_v1-training-dataqwen-9b-3m
qwen-9b-3m
Multi-domain SFT-target dataset: ~3,000,000 prompts, each with ONE completion
generated offline by Qwen/Qwen3.5-9B (thinking mode). Exported snapshot from an
offline queue pipeline; repartitioned into 512 parquet shards.
Columns (21)
record_index, input_sha256, prompt_sha256, dataset, split, source, upstream_id,
bucket, messages_json, prompt, prompt_token_count, generation_seed, enable_thinking,
worker, executor_worker, completion, completion_input_ids… See the full description on the dataset page: https://huggingface.co/datasets/dipta007/qwen-9b-3m.openwebtext-tokenized-9b
Dataset Card for "openwebtext-tokenized-9b"
More Information needed
details_Azure99__blossom-v5-9b
Dataset Card for Evaluation run of Azure99/blossom-v5-9b
Dataset automatically created during the evaluation run of model Azure99/blossom-v5-9b on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/details_Azure99__blossom-v5-9b.acm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch3
lixiaochuan2020/acm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch3
Teacher (Qwen3.5-397B-A17B) top-20 forward-KL log-prob annotations for offline on-policy
distillation (OPD) of Qwen3.5-9B on BrowseComp-Plus train680 (MemTool regime).
Trains: OPD iter-3
Annotates the rollouts of: iter-2 rollouts (…-train-rollouts-…-epoch2)
One .npz per (question, rep) trajectory · 736 files.
Schema (per file, numpy.load)
key
shape
dtype
meaning
input_ids
(L,)
int32… See the full description on the dataset page: https://huggingface.co/datasets/lixiaochuan2020/acm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch3.llama-9b-bulk-npzacm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch2
lixiaochuan2020/acm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch2
Teacher (Qwen3.5-397B-A17B) top-20 forward-KL log-prob annotations for offline on-policy
distillation (OPD) of Qwen3.5-9B on BrowseComp-Plus train680 (MemTool regime).
Trains: OPD iter-2
Annotates the rollouts of: iter-1 rollouts (…-train-rollouts-…-epoch1)
One .npz per (question, rep) trajectory · 849 files.
Schema (per file, numpy.load)
key
shape
dtype
meaning
input_ids
(L,)
int32… See the full description on the dataset page: https://huggingface.co/datasets/lixiaochuan2020/acm-browsecompplus-teacher-logprobs-qwen3.5-9b-epoch2.gemma2_9b_it_taboo_salt_oracle_v1-training-dataQwythos-9B-Claude-Mythos-5-1M-atlas
juiceb0xc0de/Qwythos-9B-Claude-Mythos-5-1M-atlas
A brain atlas for empero-ai/Qwythos-9B-Claude-Mythos-5-1M, a 32-layer hybrid that runs linear attention on 24 layers and full attention on the other 8. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
The interesting thing about this model is how little of it is full… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Qwythos-9B-Claude-Mythos-5-1M-atlas.gemma2_9b_it_taboo_green_oracle_v1-training-datagemma2_9b_it_taboo_chair_oracle_v1-training-datagemma2_9b_it_taboo_flag_oracle_v1-training-datagemma2_9b_it_taboo_song_oracle_v1-training-datagemma2_9b_it_taboo_ship_oracle_v1-training-dataafrica-drc-congo-dem-rep-environment-9b29113f
Congo, Dem. Rep. - Environment | Africa (DRC official open data)
4,653 rows - 1 Africa country - 1960-2025 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official CSV resource from DRC as
ML-ready Parquet. The source file is the provenance boundary; all usable
indicators or tabular columns from the resource stay together in this repo.
About the source
Source: Congo, Dem. Rep. - Environment
Publisher: World Bank Group… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-drc-congo-dem-rep-environment-9b29113f.gemma2_9b_it_taboo_book_oracle_v1-training-data