datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tmax-27b-atlas
juiceb0xc0de/tmax-27b-atlas
A brain atlas for allenai/tmax-27b, the largest member of the tmax hybrid SSM/Mamba/transformer family. This model required custom kernel work to fit into the atlas pipeline, and the result is the deepest, most redundant, and most surgically forgiving atlas in the family.
What was run
Model: allenai/tmax-27b
Corpus: 8,965 diverse prompts
Layers probed: all 64
Attention layers: 3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/tmax-27b-atlas.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.tmax-2b-atlas
juiceb0xc0de/tmax-2b-atlas
A brain atlas for allenai/tmax-2b, a hybrid SSM/Mamba/transformer language model. This is not a chat dataset or a benchmark — it is an internal-mechanics map of the model, 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 where the model stores compliance style, which late-layer directions you can edit without breaking reasoning, or whether the… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/tmax-2b-atlas.tmax-4b-atlas
juiceb0xc0de/tmax-4b-atlas
A brain atlas for allenai/tmax-4b, the mid-entry hybrid SSM/Mamba/transformer language model from the tmax family. This is not a chat dataset or a benchmark — it is an internal-mechanics map of the model, 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 where the model stores compliance style, which late-layer directions you can edit without… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/tmax-4b-atlas.Segformer_b0_tmax_expSegformer_10Phases_Inference_Tmax1-10